Playback of a stored networked remote collaboration session

ABSTRACT

Various implementations of the present application set forth a method comprising generating three-dimensional data and two-dimensional data representing a physical space that includes a real-world asset, generating an extended-reality (XR) stream representing a remote collaboration session between a host device and a set of remote devices, where the XR stream includes a combination of the three-dimensional data and the two-dimensional data, a set of augmented-reality (AR) elements associated with the real-world asset, and a set of performed actions associated with a portion of the digital representation or at least one AR element, serializing the XR stream into a set of serialized chunks, transmitting the serialized chunks to the remote devices, where the remote devices recreate the XR stream in a set of remote XR environments, and transmitting the serialized chunks to a remote storage device, where a device subsequently retrieves the serialized chunks to replay the remote collaboration session.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Applicationhaving Ser. No. 63/093,111 and filed on Oct. 16, 2020, U.S. ProvisionalApplication having Ser. No. 63/093,123 and filed on Oct. 16, 2020, andU.S. Provisional Application No. 63/093,143 and filed on Oct. 16, 2020.The subject matter of these related applications is hereby incorporatedherein by reference.

BACKGROUND

The present disclosure relates generally to computer networks, and morespecifically, to sharing data for remote collaboration in extendedreality environments.

Many information technology (IT) environments enable the access ofmassive quantities of diverse data stored across multiple data sources.For example, an IT environment may enable users to access textdocuments, user-generated data stored in a variety of relationaldatabase management systems, and machine-generated data stored insystems, such as SPLUNK® ENTERPRISE systems. While the availability ofmassive quantities of diverse data provides opportunities to derive newinsights that increase the usefulness and value of IT systems, a commonproblem associated with IT environments is that curating, searching, andanalyzing the data is quite technically challenging.

In particular, multiple users have difficulty interacting withreal-world environments remotely. Various conventional approaches enableone user to capture an image or video and share a stream of the videowith remote users via remote devices. However, the remote user islimited by the view that is presented by the host, limiting the abilityof the remote user to analyze a physical object using the remote device.

As the foregoing illustrates, what is needed in the art are moreefficient techniques of remote interaction with a real-worldenvironment.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the inventioncan be understood in detail, a more particular description of theinvention may be had by reference to implementations, some of which areillustrated in the appended drawings. It is to be noted, however, thatthe appended drawings illustrate only typical implementations of thisinvention and are therefore not to be considered limiting of its scope,for the invention may admit to other equally effective implementations.

The present disclosure is illustrated by way of example, and notlimitation, in the figures of the accompanying drawings, in which likereference numerals indicate similar elements and in which:

FIG. 1 is a block diagram of an example networked computer environment,in accordance with example implementations;

FIG. 2 is a block diagram of an example data intake and query system, inaccordance with example implementations;

FIG. 3 is a block diagram of an example cloud-based data intake andquery system, in accordance with example implementations;

FIG. 4 is a block diagram of an example data intake and query systemthat performs searches across external data systems, in accordance withexample implementations;

FIG. 5A is a flowchart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example implementations;

FIG. 5B is a block diagram of a data structure in which time-stampedevent data can be stored in a data store, in accordance with exampleimplementations;

FIG. 5C provides a visual representation of the manner in which apipelined search language or query operates, in accordance with exampleimplementations;

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample implementations;

FIG. 6B provides a visual representation of an example manner in which apipelined command language or query operates, in accordance with exampleimplementations;

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example implementations;

FIG. 7B illustrates an example of processing keyword searches and fieldsearches, in accordance with disclosed implementations;

FIG. 7C illustrates an example of creating and using an inverted index,in accordance with example implementations;

FIG. 7D depicts a flowchart of example use of an inverted index in apipelined search query, in accordance with example implementations;

FIG. 8A is an interface diagram of an example user interface for asearch screen, in accordance with example implementations;

FIG. 8B is an interface diagram of an example user interface for a datasummary dialog that enables a user to select various data sources, inaccordance with example implementations;

FIGS. 9-15 are interface diagrams of example report generation userinterfaces, in accordance with example implementations;

FIG. 16 is an example search query received from a client and executedby search peers, in accordance with example implementations;

FIG. 17A is an interface diagram of an example user interface of a keyindicators view, in accordance with example implementations;

FIG. 17B is an interface diagram of an example user interface of anincident review dashboard, in accordance with example implementations;

FIG. 17C is a tree diagram of an example a proactive monitoring tree, inaccordance with example implementations;

FIG. 17D is an interface diagram of an example a user interfacedisplaying both log data and performance data, in accordance withexample implementations;

FIG. 18A illustrates a network architecture that enables securecommunications between an extended reality application, a mobileoperations application, and an on-premises environment for the dataintake and query system, in accordance with example implementations.

FIG. 18B illustrates a more-detailed view of the example networkedcomputer environment of FIG. 1 , in accordance with exampleimplementations.

FIG. 19 illustrates a block diagram of an example networked computerenvironment of FIG. 1 , in accordance with example implementations.

FIG. 20 illustrates an example extended reality environment thatpresents information using the networked computer environment, inaccordance with example implementations.

FIG. 21A illustrates a menu with selectable icons to conduct variousoperations associated with the real-world environment and/or dataprocessing service, in accordance with example implementations.

FIG. 21B illustrates a menu with selectable icons to initiate a remotecollaboration, in accordance with example implementations.

FIG. 21C, illustrates the extendable application scanning a physicalspace within a real-world environment, in accordance with exampleimplementations.

FIG. 21D illustrates the extendable application completing a scan of aphysical space, in accordance with example implementations.

FIG. 21E illustrates an invitation menu for a potential participant in aremote collaboration session, in accordance with exampleimplementations.

FIG. 21F illustrates a portion of a remote environment during a remotecollaboration session, in accordance with example implementations.

FIG. 22A illustrates a menu with selectable icons to initiate a remotecollaboration, in accordance with example implementations.

FIG. 22B illustrates a splash page describing the remote collaborationsession, in accordance with example implementations.

FIG. 22C illustrates a portion of the remote XR environment portion, inaccordance with example implementations.

FIG. 22D illustrates a portion of remote XR environment portion at alater time during the remote collaboration session, in accordance withexample implementations.

FIG. 23 sets forth a flow diagram of method steps for providing anextended reality stream for a remote collaboration session, inaccordance with example implementations.

FIG. 24 sets forth a flow diagram of method steps for generating andinteracting with a digital representation of a physical space, inaccordance with example implementations.

FIG. 25 illustrates a call flow diagram showing interactions betweenvarious components of the example networked computing environment, inaccordance with example implementations.

FIG. 26 illustrates a call flow diagram showing interactions betweenvarious components of the example networked computing environment, inaccordance with example implementations.

FIG. 27 sets forth a flow diagram of method steps for providing arecorded remote collaboration session, in accordance with exampleimplementations.

DETAILED DESCRIPTION

Examples and implementations are described herein according to thefollowing outline:

1. General Overview

2. Operating Environment

2.1 Host Devices

2.2 Client Devices

2.3 Client Device Applications

2.4 Data Server System

2.5 Cloud-Based System Overview

2.6 Searching Externally-Archived Data

-   -   2.6.1 ERP Process Features

2.7 Data Ingestion

-   -   2.7.1 Input    -   2.7.2 Parsing    -   2.7.3 Indexing

2.8 Query Processing

2.9 Pipelined Search Language

2.10 Field Extraction

2.11 Example Search Screen

2.12 Data Models

2.13 Acceleration Technique

-   -   2.13.1 Aggregation Technique    -   2.13.2 Keyword Index    -   2.13.3 High Performance Analytics Store        -   2.13.3.1 Extracting Event Data Using Posting        -   2.13.3.2 Accelerating Report Generation

2.14 Security Features

2.15 Data Center Monitoring

3. Sharing Physical Data for Remote Collaboration Sessions

3.1 Networked Remote Collaboration System

3.2 Storing Remote Collaboration Session Data

1. General Overview

Modern data centers and other computing environments can compriseanywhere from a few host computer systems to thousands of systemsconfigured to process data, service requests from remote clients, andperform numerous other computational tasks. During operation, variouscomponents within these computing environments often generatesignificant volumes of machine data. Machine data is any data producedby a machine or component in an information technology (IT) environmentand that reflects activity in the IT environment. For example, machinedata can be raw machine data that is generated by various components inIT environments, such as servers, sensors, routers, mobile devices,Internet of Things (IoT) devices, etc. Machine data can include systemlogs, network packet data, sensor data, application program data, errorlogs, stack traces, system performance data, etc. In general, machinedata can also include performance data, diagnostic information, and manyother types of data that can be analyzed to diagnose performanceproblems, monitor user interactions, and to derive other insights.

A number of tools are available to analyze machine data. In order toreduce the size of the potentially vast amount of machine data that maybe generated, many of these tools typically pre-process the data basedon anticipated data-analysis needs. For example, pre-specified dataitems may be extracted from the machine data and stored in a database tofacilitate efficient retrieval and analysis of those data items atsearch time. However, the rest of the machine data typically is notsaved and is discarded during pre-processing. As storage capacitybecomes progressively cheaper and more plentiful, there are fewerincentives to discard these portions of machine data and many reasons toretain more of the data.

This plentiful storage capacity is presently making it feasible to storemassive quantities of minimally processed machine data for laterretrieval and analysis. In general, storing minimally processed machinedata and performing analysis operations at search time can providegreater flexibility because it enables an analyst to search all of themachine data, instead of searching only a pre-specified set of dataitems. This may enable an analyst to investigate different aspects ofthe machine data that previously were unavailable for analysis.

However, analyzing and searching massive quantities of machine datapresents a number of challenges. For example, a data center, servers, ornetwork appliances may generate many different types and formats ofmachine data (e.g., system logs, network packet data (e.g., wire data,etc.), sensor data, application program data, error logs, stack traces,system performance data, operating system data, virtualization data,etc.) from thousands of different components, which can collectively bevery time-consuming to analyze. In another example, mobile devices maygenerate large amounts of information relating to data accesses,application performance, operating system performance, networkperformance, etc. There can be millions of mobile devices that reportthese types of information.

These challenges can be addressed by using an event-based data intakeand query system, such as the SPLUNK® ENTERPRISE system developed bySplunk Inc. of San Francisco, Calif. The SPLUNK® ENTERPRISE system isthe leading platform for providing real-time operational intelligencethat enables organizations to collect, index, and search machine datafrom various websites, applications, servers, networks, and mobiledevices that power their businesses. The data intake and query system isparticularly useful for analyzing data which is commonly found in systemlog files, network data, and other data input sources. Although many ofthe techniques described herein are explained with reference to a dataintake and query system similar to the SPLUNK® ENTERPRISE system, thesetechniques are also applicable to other types of data systems.

In the data intake and query system, machine data are collected andstored as “events”. An event comprises a portion of machine data and isassociated with a specific point in time. The portion of machine datamay reflect activity in an IT environment and may be produced by acomponent of that IT environment, where the events may be searched toprovide insight into the IT environment, thereby improving theperformance of components in the IT environment. Events may be derivedfrom “time series data,” where the time series data comprises a sequenceof data points (e.g., performance measurements from a computer system,etc.) that are associated with successive points in time. In general,each event has a portion of machine data that is associated with atimestamp that is derived from the portion of machine data in the event.A timestamp of an event may be determined through interpolation betweentemporally proximate events having known timestamps or may be determinedbased on other configurable rules for associating timestamps withevents.

In some instances, machine data can have a predefined format, where dataitems with specific data formats are stored at predefined locations inthe data. For example, the machine data may include data associated withfields in a database table. In other instances, machine data may nothave a predefined format (e.g., may not be at fixed, predefinedlocations), but may have repeatable (e.g., non-random) patterns. Thismeans that some machine data can comprise various data items ofdifferent data types that may be stored at different locations withinthe data. For example, when the data source is an operating system log,an event can include one or more lines from the operating system logcontaining machine data that includes different types of performance anddiagnostic information associated with a specific point in time (e.g., atimestamp).

Examples of components which may generate machine data from which eventscan be derived include, but are not limited to, web servers, applicationservers, databases, firewalls, routers, operating systems, and softwareapplications that execute on computer systems, mobile devices, sensors,Internet of Things (IoT) devices, etc. The machine data generated bysuch data sources can include, for example and without limitation,server log files, activity log files, configuration files, messages,network packet data, performance measurements, sensor measurements, etc.

The data intake and query system uses a flexible schema to specify howto extract information from events. A flexible schema may be developedand redefined as needed. Note that a flexible schema may be applied toevents “on the fly,” when it is needed (e.g., at search time, indextime, ingestion time, etc.). When the schema is not applied to eventsuntil search time, the schema may be referred to as a “late-bindingschema.”

During operation, the data intake and query system receives machine datafrom any type and number of sources (e.g., one or more system logs,streams of network packet data, sensor data, application program data,error logs, stack traces, system performance data, etc.). The systemparses the machine data to produce events each having a portion ofmachine data associated with a timestamp. The system stores the eventsin a data store. The system enables users to run queries against thestored events to, for example, retrieve events that meet criteriaspecified in a query, such as criteria indicating certain keywords orhaving specific values in defined fields. As used herein, the term“field” refers to a location in the machine data of an event containingone or more values for a specific data item. A field may be referencedby a field name associated with the field. As will be described in moredetail herein, a field is defined by an extraction rule (e.g., a regularexpression) that derives one or more values or a sub-portion of textfrom the portion of machine data in each event to produce a value forthe field for that event. The set of values produced aresemantically-related (such as IP address), even though the machine datain each event may be in different formats (e.g., semantically-relatedvalues may be in different positions in the events derived fromdifferent sources).

As described above, the system stores the events in a data store. Theevents stored in the data store are field-searchable, wherefield-searchable herein refers to the ability to search the machine data(e.g., the raw machine data) of an event based on a field specified insearch criteria. For example, a search having criteria that specifies afield name “UserID” may cause the system to field-search the machinedata of events to identify events that have the field name “UserID.” Inanother example, a search having criteria that specifies a field name“UserID” with a corresponding field value “12345” may cause the systemto field-search the machine data of events to identify events havingthat field-value pair (e.g., field name “UserID” with a correspondingfield value of “12345”). Events are field-searchable using one or moreconfiguration files associated with the events. Each configuration fileincludes one or more field names, where each field name is associatedwith a corresponding extraction rule and a set of events to which thatextraction rule applies. The set of events to which an extraction ruleapplies may be identified by metadata associated with the set of events.For example, an extraction rule may apply to a set of events that areeach associated with a particular host, source, or source type. Whenevents are to be searched based on a particular field name specified ina search, the system uses one or more configuration files to determinewhether there is an extraction rule for that particular field name thatapplies to each event that falls within the criteria of the search. Ifso, the event is considered as part of the search results (andadditional processing may be performed on that event based on criteriaspecified in the search). If not, the next event is similarly analyzed,and so on.

As noted above, the data intake and query system utilizes a late-bindingschema while performing queries on events. One aspect of a late-bindingschema is applying extraction rules to events to extract values forspecific fields during search time. More specifically, the extractionrule for a field can include one or more instructions that specify howto extract a value for the field from an event. An extraction rule cangenerally include any type of instruction for extracting values fromevents. In some cases, an extraction rule comprises a regularexpression, where a sequence of characters form a search pattern. Anextraction rule comprising a regular expression is referred to herein asa regex rule. The system applies a regex rule to an event to extractvalues for a field associated with the regex rule, where the values areextracted by searching the event for the sequence of characters definedin the regex rule.

In the data intake and query system, a field extractor may be configuredto automatically generate extraction rules for certain fields in theevents when the events are being created, indexed, or stored, orpossibly at a later time. Alternatively, a user may manually defineextraction rules for fields using a variety of techniques. In contrastto a conventional schema for a database system, a late-binding schema isnot defined at data ingestion time. Instead, the late-binding schema canbe developed on an ongoing basis until the time a query is actuallyexecuted. This means that extraction rules for the fields specified in aquery may be provided in the query itself, or may be located duringexecution of the query. Hence, as a user learns more about the data inthe events, the user can continue to refine the late-binding schema byadding new fields, deleting fields, or modifying the field extractionrules for use the next time the schema is used by the system. Becausethe data intake and query system maintains the underlying machine dataand uses a late-binding schema for searching the machine data, itenables a user to continue investigating and learn valuable insightsabout the machine data.

In some implementations, a common field name may be used to referencetwo or more fields containing equivalent and/or similar data items, eventhough the fields may be associated with different types of events thatpossibly have different data formats and different extraction rules. Byenabling a common field name to be used to identify equivalent and/orsimilar fields from different types of events generated by disparatedata sources, the system facilitates use of a “common information model”(CIM) across the disparate data sources (further discussed with respectto FIG. 7A).

2. Operating Environment

FIG. 1 is a block diagram of an example networked computer environment100, in accordance with example implementations. Those skilled in theart would understand that FIG. 1 represents one example of a networkedcomputer system and other implementations may use differentarrangements.

The networked computer system 100 comprises one or more computingdevices. These one or more computing devices comprise any combination ofhardware and software configured to implement the various logicalcomponents described herein. For example, the one or more computingdevices may include one or more memories that store instructions forimplementing the various components described herein, one or morehardware processors configured to execute the instructions stored in theone or more memories, and various data repositories in the one or morememories for storing data structures utilized and manipulated by thevarious components.

In some implementations, one or more client devices 102 are coupled toone or more host devices 106 and a data intake and query system 108 viaone or more networks 104. Networks 104 broadly represent one or moreLANs, WANs, cellular networks (e.g., LTE, HSPA, 3G, and other cellulartechnologies), and/or networks using any of wired, wireless, terrestrialmicrowave, or satellite links, and may include the public Internet.

2.1 Host Devices

In the illustrated implementation, a system 100 includes one or morehost devices 106. Host devices 106 may broadly include any number ofcomputers, virtual machine instances, and/or data centers that areconfigured to host or execute one or more instances of host applications114. In general, a host device 106 may be involved, directly orindirectly, in processing requests received from client devices 102.Each host device 106 may comprise, for example, one or more of a networkdevice, a web server, an application server, a database server, etc. Acollection of host devices 106 may be configured to implement anetwork-based service. For example, a provider of a network-basedservice may configure one or more host devices 106 and host applications114 (e.g., one or more web servers, application servers, databaseservers, etc.) to collectively implement the network-based application.

In general, client devices 102 communicate with one or more hostapplications 114 to exchange information. The communication between aclient device 102 and a host application 114 may, for example, be basedon the Hypertext Transfer Protocol (HTTP) or any other network protocol.Content delivered from the host application 114 to a client device 102may include, for example, HTML documents, media content, etc. Thecommunication between a client device 102 and host application 114 mayinclude sending various requests and receiving data packets. Forexample, in general, a client device 102 or application running on aclient device may initiate communication with a host application 114 bymaking a request for a specific resource (e.g., based on an HTTPrequest), and the application server may respond with the requestedcontent stored in one or more response packets.

In the illustrated implementation, one or more of host applications 114may generate various types of performance data during operation,including event logs, network data, sensor data, and other types ofmachine data. For example, a host application 114 comprising a webserver may generate one or more web server logs in which details ofinteractions between the web server and any number of client devices 102is recorded. As another example, a host device 106 comprising a routermay generate one or more router logs that record information related tonetwork traffic managed by the router. As yet another example, a hostapplication 114 comprising a database server may generate one or morelogs that record information related to requests sent from other hostapplications 114 (e.g., web servers or application servers) for datamanaged by the database server.

2.2 Client Devices

Client devices 102 of FIG. 1 represent any computing device capable ofinteracting with one or more host devices 106 via a network 104.Examples of client devices 102 may include, without limitation,smartphones, tablet computers, handheld computers, wearable devices,laptop computers, desktop computers, servers, portable media players,gaming devices, and so forth. In general, a client device 102 canprovide access to different content, for instance, content provided byone or more host devices 106, etc. Each client device 102 may compriseone or more client applications 110, described in more detail in aseparate section hereinafter.

2.3 Client Device Applications

In some implementations, each client device 102 may host or execute oneor more client applications 110 that are capable of interacting with oneor more host devices 106 via one or more networks 104. For instance, aclient application 110 may be or comprise a web browser that a user mayuse to navigate to one or more websites or other resources provided byone or more host devices 106. As another example, a client application110 may comprise a mobile application or “app.” For example, an operatorof a network-based service hosted by one or more host devices 106 maymake available one or more mobile apps that enable users of clientdevices 102 to access various resources of the network-based service. Asyet another example, client applications 110 may include backgroundprocesses that perform various operations without direct interactionfrom a user. A client application 110 may include a “plug-in” or“extension” to another application, such as a web browser plug-in orextension.

In some implementations, a client application 110 may include amonitoring component 112. At a high level, the monitoring component 112comprises a software component or other logic that facilitatesgenerating performance data related to a client device's operatingstate, including monitoring network traffic sent and received from theclient device and collecting other device and/or application-specificinformation. Monitoring component 112 may be an integrated component ofa client application 110, a plug-in, an extension, or any other type ofadd-on component. Monitoring component 112 may also be a stand-aloneprocess.

In some implementations, a monitoring component 112 may be created whena client application 110 is developed, for example, by an applicationdeveloper using a software development kit (SDK). The SDK may includecustom monitoring code that can be incorporated into the codeimplementing a client application 110. When the code is converted to anexecutable application, the custom code implementing the monitoringfunctionality can become part of the application itself.

In some implementations, an SDK or other code for implementing themonitoring functionality may be offered by a provider of a data intakeand query system, such as a system 108. In such cases, the provider ofthe system 108 can implement the custom code so that performance datagenerated by the monitoring functionality is sent to the system 108 tofacilitate analysis of the performance data by a developer of the clientapplication or other users.

In some implementations, the custom monitoring code may be incorporatedinto the code of a client application 110 in a number of different ways,such as the insertion of one or more lines in the client applicationcode that call or otherwise invoke the monitoring component 112. Assuch, a developer of a client application 110 can add one or more linesof code into the client application 110 to trigger the monitoringcomponent 112 at desired points during execution of the application.Code that triggers the monitoring component may be referred to as amonitor trigger. For instance, a monitor trigger may be included at ornear the beginning of the executable code of the client application 110such that the monitoring component 112 is initiated or triggered as theapplication is launched, or included at other points in the code thatcorrespond to various actions of the client application, such as sendinga network request or displaying a particular interface.

In some implementations, the monitoring component 112 may monitor one ormore aspects of network traffic sent and/or received by a clientapplication 110. For example, the monitoring component 112 may beconfigured to monitor data packets transmitted to and/or from one ormore host applications 114. Incoming and/or outgoing data packets can beread or examined to identify network data contained within the packets,for example, and other aspects of data packets can be analyzed todetermine a number of network performance statistics. Monitoring networktraffic may enable information to be gathered particular to the networkperformance associated with a client application 110 or set ofapplications.

In some implementations, network performance data refers to any type ofdata that indicates information about the network and/or networkperformance. Network performance data may include, for instance, a URLrequested, a connection type (e.g., HTTP, HTTPS, etc.), a connectionstart time, a connection end time, an HTTP status code, request length,response length, request headers, response headers, connection status(e.g., completion, response time(s), failure, etc.), and the like. Uponobtaining network performance data indicating performance of thenetwork, the network performance data can be transmitted to a dataintake and query system 108 for analysis.

Upon developing a client application 110 that incorporates a monitoringcomponent 112, the client application 110 can be distributed to clientdevices 102. Applications generally can be distributed to client devices102 in any manner, or they can be pre-loaded. In some cases, theapplication may be distributed to a client device 102 via an applicationmarketplace or other application distribution system. For instance, anapplication marketplace or other application distribution system mightdistribute the application to a client device based on a request fromthe client device to download the application.

Examples of functionality that enables monitoring performance of aclient device are described in U.S. patent application Ser. No.14/524,748, entitled “UTILIZING PACKET HEADERS TO MONITOR NETWORKTRAFFIC IN ASSOCIATION WITH A CLIENT DEVICE”, filed on 27 Oct. 2014, andwhich is hereby incorporated by reference in its entirety for allpurposes.

In some implementations, the monitoring component 112 may also monitorand collect performance data related to one or more aspects of theoperational state of a client application 110 and/or client device 102.For example, a monitoring component 112 may be configured to collectdevice performance information by monitoring one or more client deviceoperations, or by making calls to an operating system and/or one or moreother applications executing on a client device 102 for performanceinformation. Device performance information may include, for instance, acurrent wireless signal strength of the device, a current connectiontype and network carrier, current memory performance information, ageographic location of the device, a device orientation, and any otherinformation related to the operational state of the client device.

In some implementations, the monitoring component 112 may also monitorand collect other device profile information including, for example, atype of client device, a manufacturer and model of the device, versionsof various software applications installed on the device, and so forth.

In general, a monitoring component 112 may be configured to generateperformance data in response to a monitor trigger in the code of aclient application 110 or other triggering application event, asdescribed above, and to store the performance data in one or more datarecords. Each data record, for example, may include a collection offield-value pairs, each field-value pair storing a particular item ofperformance data in association with a field for the item. For example,a data record generated by a monitoring component 112 may include a“networkLatency” field (not shown in FIG. 1 ) in which a value isstored. This field indicates a network latency measurement associatedwith one or more network requests. The data record may include a “state”field to store a value indicating a state of a network connection, andso forth for any number of aspects of collected performance data.

2.4 Data Server System

FIG. 2 is a block diagram of an example data intake and query system108, in accordance with example implementations. System 108 includes oneor more forwarders 204 that receive data from a variety of input datasources 202, and one or more indexers 206 that process and store thedata in one or more data stores 208. These forwarders 204 and indexers208 can comprise separate computer systems, or may alternativelycomprise separate processes executing on one or more computer systems.

Each data source 202 broadly represents a distinct source of data thatcan be consumed by system 108. Examples of data sources 202 include,without limitation, data files, directories of files, data sent over anetwork, event logs, registries, etc.

During operation, the forwarders 204 identify which indexers 206 receivedata collected from a data source 202 and forward the data to theappropriate indexers. Forwarders 204 can also perform operations on thedata before forwarding, including removing extraneous data, detectingtimestamps in the data, parsing data, indexing data, routing data basedon criteria relating to the data being routed, and/or performing otherdata transformations.

In some implementations, a forwarder 204 may comprise a serviceaccessible to client devices 102 and host devices 106 via a network 104.For example, one type of forwarder 204 may be capable of consuming vastamounts of real-time data from a potentially large number of clientdevices 102 and/or host devices 106. The forwarder 204 may, for example,comprise a computing device which implements multiple data pipelines or“queues” to handle forwarding of network data to indexers 206. Aforwarder 204 may also perform many of the functions that are performedby an indexer. For example, a forwarder 204 may perform keywordextractions on raw data or parse raw data to create events. A forwarder204 may generate time stamps for events. Additionally or alternatively,a forwarder 204 may perform routing of events to indexers 206. Datastore 208 may contain events derived from machine data from a variety ofsources all pertaining to the same component in an IT environment, andthis data may be produced by the machine in question or by othercomponents in the IT environment.

2.5 Cloud-Based System Overview

The example data intake and query system 108 described in reference toFIG. 2 comprises several system components, including one or moreforwarders, indexers, and search heads. In some environments, a user ofa data intake and query system 108 may install and configure, oncomputing devices owned and operated by the user, one or more softwareapplications that implement some or all of these system components. Forexample, a user may install a software application on server computersowned by the user and configure each server to operate as one or more ofa forwarder, an indexer, a search head, etc. This arrangement generallymay be referred to as an “on-premises” solution. That is, the system 108is installed and operates on computing devices directly controlled bythe user of the system. Some users may prefer an on-premises solutionbecause it may provide a greater level of control over the configurationof certain aspects of the system (e.g., security, privacy, standards,controls, etc.). However, other users may instead prefer an arrangementin which the user is not directly responsible for providing and managingthe computing devices upon which various components of system 108operate.

In one implementation, to provide an alternative to an entirelyon-premises environment for system 108, one or more of the components ofa data intake and query system instead may be provided as a cloud-basedservice. In this context, a cloud-based service refers to a servicehosted by one more computing resources that are accessible to end usersover a network, for example, by using a web browser or other applicationon a client device to interface with the remote computing resources. Forexample, a service provider may provide a cloud-based data intake andquery system by managing computing resources configured to implementvarious aspects of the system (e.g., forwarders, indexers, search heads,etc.) and by providing access to the system to end users via a network.Typically, a user may pay a subscription or other fee to use such aservice. Each subscribing user of the cloud-based service may beprovided with an account that enables the user to configure a customizedcloud-based system based on the user's preferences.

FIG. 3 illustrates a block diagram of an example cloud-based data intakeand query system. Similar to the system of FIG. 2 , the networkedcomputer system 300 includes input data sources 202 and forwarders 204.These input data sources and forwarders may be in a subscriber's privatecomputing environment. Alternatively, they might be directly managed bythe service provider as part of the cloud service. In the example system300, one or more forwarders 204 and client devices 302 are coupled to acloud-based data intake and query system 306 via one or more networks304. Network 304 broadly represents one or more LANs, WANs, cellularnetworks, intranetworks, internetworks, etc., using any of wired,wireless, terrestrial microwave, satellite links, etc., and may includethe public Internet, and is used by client devices 302 and forwarders204 to access the system 306. Similar to the system of 38, each of theforwarders 204 may be configured to receive data from an input sourceand to forward the data to other components of the system 306 forfurther processing.

In some implementations, a cloud-based data intake and query system 306may comprise a plurality of system instances 308. In general, eachsystem instance 308 may include one or more computing resources managedby a provider of the cloud-based system 306 made available to aparticular subscriber. The computing resources comprising a systeminstance 308 may, for example, include one or more servers or otherdevices configured to implement one or more forwarders, indexers, searchheads, and other components of a data intake and query system, similarto system 108. As indicated above, a subscriber may use a web browser orother application of a client device 302 to access a web portal or otherinterface that enables the subscriber to configure an instance 308.

Providing a data intake and query system as described in reference tosystem 108 as a cloud-based service presents a number of challenges.Each of the components of a system 108 (e.g., forwarders, indexers, andsearch heads) may at times refer to various configuration files storedlocally at each component. These configuration files typically mayinvolve some level of user configuration to accommodate particular typesof data a user desires to analyze and to account for other userpreferences. However, in a cloud-based service context, users typicallymay not have direct access to the underlying computing resourcesimplementing the various system components (e.g., the computingresources comprising each system instance 308) and may desire to makesuch configurations indirectly, for example, using one or more web-basedinterfaces. Thus, the techniques and systems described herein forproviding user interfaces that enable a user to configure source typedefinitions are applicable to both on-premises and cloud-based servicecontexts, or some combination thereof (e.g., a hybrid system where bothan on-premises environment, such as SPLUNK® ENTERPRISE, and acloud-based environment, such as SPLUNK CLOUD™, are centrally visible).

2.6 Searching Externally-Archived Data

FIG. 4 shows a block diagram of an example of a data intake and querysystem 108 that provides transparent search facilities for data systemsthat are external to the data intake and query system. Such facilitiesare available in the Splunk® Analytics for Hadoop® system provided bySplunk Inc. of San Francisco, Calif. Splunk® Analytics for Hadoop®represents an analytics platform that enables business and IT teams torapidly explore, analyze, and visualize data in Hadoop® and NoSQL datastores.

The search head 210 of the data intake and query system receives searchrequests from one or more client devices 404 over network connections420. As discussed above, the data intake and query system 108 may residein an enterprise location, in the cloud, etc. FIG. 4 illustrates thatmultiple client devices 404 a, 404 b, . . . , 404 n may communicate withthe data intake and query system 108. The client devices 404 maycommunicate with the data intake and query system using a variety ofconnections. For example, one client device in FIG. 4 is illustrated ascommunicating over an Internet (Web) protocol, another client device isillustrated as communicating via a command line interface, and anotherclient device is illustrated as communicating via a software developerkit (SDK).

The search head 210 analyzes the received search request to identifyrequest parameters. If a search request received from one of the clientdevices 404 references an index maintained by the data intake and querysystem, then the search head 210 connects to one or more indexers 206 ofthe data intake and query system for the index referenced in the requestparameters. That is, if the request parameters of the search requestreference an index, then the search head accesses the data in the indexvia the indexer. The data intake and query system 108 may include one ormore indexers 206, depending on system access resources andrequirements. As described further below, the indexers 206 retrieve datafrom their respective local data stores 208 as specified in the searchrequest. The indexers and their respective data stores can comprise oneor more storage devices and typically reside on the same system, thoughthey may be connected via a local network connection.

If the request parameters of the received search request reference anexternal data collection, which is not accessible to the indexers 206 orunder the management of the data intake and query system, then thesearch head 210 can access the external data collection through anExternal Result Provider (ERP) process 410. An external data collectionmay be referred to as a “virtual index” (plural, “virtual indices”). AnERP process provides an interface through which the search head 210 mayaccess virtual indices.

Thus, a search reference to an index of the system relates to a locallystored and managed data collection. In contrast, a search reference to avirtual index relates to an externally stored and managed datacollection, which the search head may access through one or more ERPprocesses 410, 412. FIG. 4 shows two ERP processes 410, 412 that connectto respective remote (external) virtual indices, which are indicated asa Hadoop or another system 414 (e.g., Amazon S3, Amazon EMR, otherHadoop® Compatible File Systems (HCFS), etc.) and a relational databasemanagement system (RDBMS) 416. Other virtual indices may include otherfile organizations and protocols, such as Structured Query Language(SQL) and the like. The ellipses between the ERP processes 410, 412indicate optional additional ERP processes of the data intake and querysystem 108. An ERP process may be a computer process that is initiatedor spawned by the search head 210 and is executed by the search dataintake and query system 108. Alternatively or additionally, an ERPprocess may be a process spawned by the search head 210 on the same ordifferent host system as the search head 210 resides.

The search head 210 may spawn a single ERP process in response tomultiple virtual indices referenced in a search request, or the searchhead may spawn different ERP processes for different virtual indices.Generally, virtual indices that share common data configurations orprotocols may share ERP processes. For example, all search queryreferences to a Hadoop file system may be processed by the same ERPprocess, if the ERP process is suitably configured. Likewise, all searchquery references to a SQL database may be processed by the same ERPprocess. In addition, the search head may provide a common ERP processfor common external data source types (e.g., a common vendor may utilizea common ERP process, even if the vendor includes different data storagesystem types, such as Hadoop and SQL). Common indexing schemes also maybe handled by common ERP processes, such as flat text files or Weblogfiles.

The search head 210 determines the number of ERP processes to beinitiated via the use of configuration parameters that are included in asearch request message. Generally, there is a one-to-many relationshipbetween an external results provider “family” and ERP processes. Thereis also a one-to-many relationship between an ERP process andcorresponding virtual indices that are referred to in a search request.For example, using RDBMS, assume two independent instances of such asystem by one vendor, such as one RDBMS for production and another RDBMSused for development. In such a situation, it is likely preferable (butoptional) to use two ERP processes to maintain the independent operationas between production and development data. Both of the ERPs, however,will belong to the same family, because the two RDBMS system types arefrom the same vendor.

The ERP processes 410, 412 receive a search request from the search head210. The search head may optimize the received search request forexecution at the respective external virtual index. Alternatively, theERP process may receive a search request as a result of analysisperformed by the search head or by a different system process. The ERPprocesses 410, 412 can communicate with the search head 210 viaconventional input/output routines (e.g., standard in/standard out,etc.). In this way, the ERP process receives the search request from aclient device such that the search request may be efficiently executedat the corresponding external virtual index.

The ERP processes 410, 412 may be implemented as a process of the dataintake and query system. Each ERP process may be provided by the dataintake and query system, or may be provided by process or applicationproviders who are independent of the data intake and query system. Eachrespective ERP process may include an interface application installed ata computer of the external result provider that ensures propercommunication between the search support system and the external resultprovider. The ERP processes 410, 412 generate appropriate searchrequests in the protocol and syntax of the respective virtual indices414, 416, each of which corresponds to the search request received bythe search head 210. Upon receiving search results from theircorresponding virtual indices, the respective ERP process passes theresult to the search head 210, which may return or display the resultsor a processed set of results based on the returned results to therespective client device.

Client devices 404 may communicate with the data intake and query system108 through a network interface 420, e.g., one or more LANs, WANs,cellular networks, intranetworks, and/or internetworks using any ofwired, wireless, terrestrial microwave, satellite links, etc., and mayinclude the public Internet.

The analytics platform utilizing the External Result Provider processdescribed in more detail in U.S. Pat. No. 8,738,629, entitled “ExternalResult Provided Process For Retrieving Data Stored Using A DifferentConfiguration Or Protocol”, issued on 27 May 2014, U.S. Pat. No.8,738,587, entitled “PROCESSING A SYSTEM SEARCH REQUEST BY RETRIEVINGRESULTS FROM BOTH A NATIVE INDEX AND A VIRTUAL INDEX”, issued on 25 Jul.2013, U.S. patent application Ser. No. 14/266,832, entitled “PROCESSINGA SYSTEM SEARCH REQUEST ACROSS DISPARATE DATA COLLECTION SYSTEMS”, filedon 1 May 2014, and U.S. Pat. No. 9,514,189, entitled “PROCESSING ASYSTEM SEARCH REQUEST INCLUDING EXTERNAL DATA SOURCES”, issued on 6 Dec.2016, each of which is hereby incorporated by reference in its entiretyfor all purposes.

2.6.1 ERP Process Features

The ERP processes described above may include two operation modes: astreaming mode and a reporting mode. The ERP processes can operate instreaming mode only, in reporting mode only, or in both modessimultaneously. Operating in both modes simultaneously is referred to asmixed mode operation. In a mixed mode operation, the ERP at some pointcan stop providing the search head with streaming results and onlyprovide reporting results thereafter, or the search head at some pointmay start ignoring streaming results it has been using and only usereporting results thereafter.

The streaming mode returns search results in real time, with minimalprocessing, in response to the search request. The reporting modeprovides results of a search request with processing of the searchresults prior to providing them to the requesting search head, which inturn provides results to the requesting client device. ERP operationwith such multiple modes provides greater performance flexibility withregard to report time, search latency, and resource utilization.

In a mixed mode operation, both streaming mode and reporting mode areoperating simultaneously. The streaming mode results (e.g., the machinedata obtained from the external data source) are provided to the searchhead, which can then process the results data (e.g., break the machinedata into events, timestamp it, filter it, etc.) and integrate theresults data with the results data from other external data sources,and/or from data stores of the search head. The search head performssuch processing and can immediately start returning interim (streamingmode) results to the user at the requesting client device;simultaneously, the search head is waiting for the ERP process toprocess the data it is retrieving from the external data source as aresult of the concurrently executing reporting mode.

In some instances, the ERP process initially operates in a mixed mode,such that the streaming mode operates to enable the ERP quickly toreturn interim results (e.g., some of the machined data or unprocesseddata necessary to respond to a search request) to the search head,enabling the search head to process the interim results and beginproviding to the client or search requester interim results that areresponsive to the query. Meanwhile, in this mixed mode, the ERP alsooperates concurrently in reporting mode, processing portions of machinedata in a manner responsive to the search query. Upon determining thatit has results from the reporting mode available to return to the searchhead, the ERP may halt processing in the mixed mode at that time (orsome later time) by stopping the return of data in streaming mode to thesearch head and switching to reporting mode only. The ERP at this pointstarts sending interim results in reporting mode to the search head,which in turn may then present this processed data responsive to thesearch request to the client or search requester. Typically the searchhead switches from using results from the ERP's streaming mode ofoperation to results from the ERP's reporting mode of operation when thehigher bandwidth results from the reporting mode outstrip the amount ofdata processed by the search head in the streaming mode of ERPoperation.

A reporting mode may have a higher bandwidth because the ERP does nothave to spend time transferring data to the search head for processingall the machine data. In addition, the ERP may optionally direct anotherprocessor to do the processing.

The streaming mode of operation does not need to be stopped to gain thehigher bandwidth benefits of a reporting mode; the search head maysimply stop using the streaming mode results—and start using thereporting mode results—when the bandwidth of the reporting mode hascaught up with or exceeded the amount of bandwidth provided by thestreaming mode. Thus, a variety of triggers and ways to accomplish asearch head's switch from using streaming mode results to usingreporting mode results may be appreciated by one skilled in the art.

The reporting mode can involve the ERP process (or an external system)performing event breaking, time stamping, filtering of events to matchthe search query request, and calculating statistics on the results. Theuser can request particular types of data, such as if the search queryitself involves types of events, or the search request may ask forstatistics on data, such as on events that meet the search request. Ineither case, the search head understands the query language used in thereceived query request, which may be a proprietary language. Oneexemplary query language is Splunk Processing Language (SPL) developedby the assignee of the application, Splunk Inc. The search headtypically understands how to use that language to obtain data from theindexers, which store data in a format used by the SPLUNK® Enterprisesystem.

The ERP processes support the search head, as the search head is notordinarily configured to understand the format in which data is storedin external data sources such as Hadoop or SQL data systems. Rather, theERP process performs that translation from the query submitted in thesearch support system's native format (e.g., SPL if SPLUNK® ENTERPRISEis used as the search support system) to a search query request formatthat will be accepted by the corresponding external data system. Theexternal data system typically stores data in a different format fromthat of the search support system's native index format, and it utilizesa different query language (e.g., SQL or MapReduce, rather than SPL orthe like).

As noted, the ERP process can operate in the streaming mode alone. Afterthe ERP process has performed the translation of the query request andreceived raw results from the streaming mode, the search head canintegrate the returned data with any data obtained from local datasources (e.g., native to the search support system), other external datasources, and other ERP processes (if such operations were required tosatisfy the terms of the search query). An advantage of mixed modeoperation is that, in addition to streaming mode, the ERP process isalso executing concurrently in reporting mode. Thus, the ERP process(rather than the search head) is processing query results (e.g.,performing event breaking, timestamping, filtering, possibly calculatingstatistics if required to be responsive to the search query request,etc.). It should be apparent to those skilled in the art that additionaltime is needed for the ERP process to perform the processing in such aconfiguration. Therefore, the streaming mode will allow the search headto start returning interim results to the user at the client devicebefore the ERP process can complete sufficient processing to startreturning any search results. The switchover between streaming andreporting mode happens when the ERP process determines that theswitchover is appropriate, such as when the ERP process determines itcan begin returning meaningful results from its reporting mode.

The operation described above illustrates the source of operationallatency: streaming mode has low latency (immediate results) and usuallyhas relatively low bandwidth (fewer results can be returned per unit oftime). In contrast, the concurrently running reporting mode hasrelatively high latency (it has to perform a lot more processing beforereturning any results) and usually has relatively high bandwidth (moreresults can be processed per unit of time). For example, when the ERPprocess does begin returning report results, it returns more processedresults than in the streaming mode, because, e.g., statistics only needto be calculated to be responsive to the search request. That is, theERP process doesn't have to take time to first return machine data tothe search head. As noted, the ERP process may be configured to operatein streaming mode alone and return just the machine data for the searchhead to process in a way that is responsive to the search request.Alternatively, the ERP process can be configured to operate in thereporting mode only. Also, the ERP process can be configured to operatein streaming mode and reporting mode concurrently, as described, withthe ERP process stopping the transmission of streaming results to thesearch head when the concurrently running reporting mode has caught upand started providing results. The reporting mode does not require theprocessing of all machine data that is responsive to the search queryrequest before the ERP process starts returning results; rather, thereporting mode usually performs processing of chunks of events andreturns the processing results to the search head for each chunk.

For example, an ERP process can be configured to merely return thecontents of a search result file verbatim, with little or no processingof results. That way, the search head performs all processing (such asparsing byte streams into events, filtering, etc.). The ERP process canbe configured to perform additional intelligence, such as analyzing thesearch request and handling all the computation that a native searchindexer process would otherwise perform. In this way, the configured ERPprocess provides greater flexibility in features while operatingaccording to desired preferences, such as response latency and resourcerequirements.

2.7 Data Ingestion

FIG. 5A is a flow chart of an example method that illustrates howindexers process, index, and store data received from forwarders, inaccordance with example implementations. The data flow illustrated inFIG. 5A is provided for illustrative purposes only; those skilled in theart would understand that one or more of the steps of the processesillustrated in FIG. 5A may be removed or that the ordering of the stepsmay be changed. Furthermore, for the purposes of illustrating a clearexample, one or more particular system components are described in thecontext of performing various operations during each of the data flowstages. For example, a forwarder is described as receiving andprocessing machine data during an input phase; an indexer is describedas parsing and indexing machine data during parsing and indexing phases;and a search head is described as performing a search query during asearch phase. However, other system arrangements and distributions ofthe processing steps across system components may be used.

At block 502, a forwarder receives data from an input source, such as adata source 202 shown in FIG. 2 . A forwarder initially may receive thedata as a raw data stream generated by the input source. For example, aforwarder may receive a data stream from a log file generated by anapplication server, from a stream of network data from a network device,or from any other source of data. In some implementations, a forwarderreceives the raw data and may segment the data stream into “blocks”,possibly of a uniform data size, to facilitate subsequent processingsteps.

At block 504, a forwarder or other system component annotates each blockgenerated from the raw data with one or more metadata fields. Thesemetadata fields may, for example, provide information related to thedata block as a whole and may apply to each event that is subsequentlyderived from the data in the data block. For example, the metadatafields may include separate fields specifying each of a host, a source,and a source type related to the data block. A host field may contain avalue identifying a host name or IP address of a device that generatedthe data. A source field may contain a value identifying a source of thedata, such as a pathname of a file or a protocol and port related toreceived network data. A source type field may contain a valuespecifying a particular source type label for the data. Additionalmetadata fields may also be included during the input phase, such as acharacter encoding of the data, if known, and possibly other values thatprovide information relevant to later processing steps. In someimplementations, a forwarder forwards the annotated data blocks toanother system component (typically an indexer) for further processing.

The data intake and query system allows forwarding of data from one dataintake and query instance to another, or even to a third-party system.The data intake and query system can employ different types offorwarders in a configuration.

In some implementations, a forwarder may contain the essentialcomponents needed to forward data. A forwarder can gather data from avariety of inputs and forward the data to an indexer for indexing andsearching. A forwarder can also tag metadata (e.g., source, source type,host, etc.).

In some implementations, a forwarder has the capabilities of theaforementioned forwarder as well as additional capabilities. Theforwarder can parse data before forwarding the data (e.g., can associatea time stamp with a portion of data and create an event, etc.) and canroute data based on criteria such as source or type of event. Theforwarder can also index data locally while forwarding the data toanother indexer.

2.7.2 Parsing

At block 506, an indexer receives data blocks from a forwarder andparses the data to organize the data into events. In someimplementations, to organize the data into events, an indexer maydetermine a source type associated with each data block (e.g., byextracting a source type label from the metadata fields associated withthe data block, etc.) and refer to a source type configurationcorresponding to the identified source type. The source type definitionmay include one or more properties that indicate to the indexer toautomatically determine the boundaries within the received data thatindicate the portions of machine data for events. In general, theseproperties may include regular expression-based rules or delimiter ruleswhere, for example, event boundaries may be indicated by predefinedcharacters or character strings. These predefined characters may includepunctuation marks or other special characters including, for example,carriage returns, tabs, spaces, line breaks, etc. If a source type forthe data is unknown to the indexer, an indexer may infer a source typefor the data by examining the structure of the data. Then, the indexercan apply an inferred source type definition to the data to create theevents.

At block 508, the indexer determines a timestamp for each event. Similarto the process for parsing machine data, an indexer may again refer to asource type definition associated with the data to locate one or moreproperties that indicate instructions for determining a timestamp foreach event. The properties may, for example, instruct an indexer toextract a time value from a portion of data for the event, tointerpolate time values based on timestamps associated with temporallyproximate events, to create a timestamp based on a time the portion ofmachine data was received or generated, to use the timestamp of aprevious event, or use any other rules for determining timestamps.

At block 510, the indexer associates with each event one or moremetadata fields including a field containing the timestamp determinedfor the event. In some implementations, a timestamp may be included inthe metadata fields. These metadata fields may include any number of“default fields” that are associated with all events, and may alsoinclude one more custom fields as defined by a user. Similar to themetadata fields associated with the data blocks at block 504, thedefault metadata fields associated with each event may include a host,source, and source type field including or in addition to a fieldstoring the timestamp.

At block 512, an indexer may optionally apply one or moretransformations to data included in the events created at block 506. Forexample, such transformations can include removing a portion of an event(e.g., a portion used to define event boundaries, extraneous charactersfrom the event, other extraneous text, etc.), masking a portion of anevent (e.g., masking a credit card number), removing redundant portionsof an event, etc. The transformations applied to events may, forexample, be specified in one or more configuration files and referencedby one or more source type definitions.

FIG. 5C illustrates an illustrative example of machine data can bestored in a data store in accordance with various disclosedimplementations. In other implementations, machine data can be stored ina flat file in a corresponding bucket with an associated index file,such as a time series index or “TSIDX.” As such, the depiction ofmachine data and associated metadata as rows and columns in the table ofFIG. 5C is merely illustrative and is not intended to limit the dataformat in which the machine data and metadata is stored in variousimplementations described herein. In one particular implementation,machine data can be stored in a compressed or encrypted formatted. Insuch implementations, the machine data can be stored with or beassociated with data that describes the compression or encryption schemewith which the machine data is stored. The information about thecompression or encryption scheme can be used to decompress or decryptthe machine data, and any metadata with which it is stored, at searchtime.

As mentioned above, certain metadata, e.g., host 536, source 537, sourcetype 538 and timestamps 535 can be generated for each event, andassociated with a corresponding portion of machine data 539 when storingthe event data in a data store, e.g., data store 208. Any of themetadata can be extracted from the corresponding machine data, orsupplied or defined by an entity, such as a user or computer system. Themetadata fields can become part of or stored with the event. Note thatwhile the time-stamp metadata field can be extracted from the raw dataof each event, the values for the other metadata fields may bedetermined by the indexer based on information it receives pertaining tothe source of the data separate from the machine data.

While certain default or user-defined metadata fields can be extractedfrom the machine data for indexing purposes, all the machine data withinan event can be maintained in its original condition. As such, inimplementations in which the portion of machine data included in anevent is unprocessed or otherwise unaltered, it is referred to herein asa portion of raw machine data. In other implementations, the port ofmachine data in an event can be processed or otherwise altered. As such,unless certain information needs to be removed for some reasons (e.g.extraneous information, confidential information), all the raw machinedata contained in an event can be preserved and saved in its originalform. Accordingly, the data store in which the event records are storedis sometimes referred to as a “raw record data store.” The raw recorddata store contains a record of the raw event data tagged with thevarious default fields.

In FIG. 5C, the first three rows of the table represent events 531, 532,and 533 and are related to a server access log that records requestsfrom multiple clients processed by a server, as indicated by entry of“access.log” in the source column 536.

In the example shown in FIG. 5C, each of the events 531-534 isassociated with a discrete request made from a client device. The rawmachine data generated by the server and extracted from a server accesslog can include the IP address of the client 540, the user id of theperson requesting the document 541, the time the server finishedprocessing the request 542, the request line from the client 543, thestatus code returned by the server to the client 545, the size of theobject returned to the client (in this case, the gif file requested bythe client) 546 and the time spent to serve the request in microseconds544. As seen in FIG. 5C, all the raw machine data retrieved from theserver access log is retained and stored as part of the correspondingevents, 1221, 1222, and 1223 in the data store.

Event 534 is associated with an entry in a server error log, asindicated by “error.log” in the source column 537, that records errorsthat the server encountered when processing a client request. Similar tothe events related to the server access log, all the raw machine data inthe error log file pertaining to event 534 can be preserved and storedas part of the event 534.

Saving minimally processed or unprocessed machine data in a data storeassociated with metadata fields in the manner similar to that shown inFIG. 5C is advantageous because it allows search of all the machine dataat search time instead of searching only previously specified andidentified fields or field-value pairs. As mentioned above, because datastructures used by various implementations of the present disclosuremaintain the underlying raw machine data and use a late-binding schemafor searching the raw machines data, it enables a user to continueinvestigating and learn valuable insights about the raw data. In otherwords, the user is not compelled to know about all the fields ofinformation that will be needed at data ingestion time. As a user learnsmore about the data in the events, the user can continue to refine thelate-binding schema by defining new extraction rules, or modifying ordeleting existing extraction rules used by the system.

2.7.3 Indexing

At blocks 514 and 516, an indexer can optionally generate a keywordindex to facilitate fast keyword searching for events. To build akeyword index, at block 514, the indexer identifies a set of keywords ineach event. At block 516, the indexer includes the identified keywordsin an index, which associates each stored keyword with referencepointers to events containing that keyword (or to locations withinevents where that keyword is located, other location identifiers, etc.).When an indexer subsequently receives a keyword-based query, the indexercan access the keyword index to quickly identify events containing thekeyword.

In some implementations, the keyword index may include entries for fieldname-value pairs found in events, where a field name-value pair caninclude a pair of keywords connected by a symbol, such as an equals signor colon. This way, events containing these field name-value pairs canbe quickly located. In some implementations, fields can automatically begenerated for some or all of the field names of the field name-valuepairs at the time of indexing. For example, if the string“dest=10.0.1.2” is found in an event, a field named “dest” may becreated for the event, and assigned a value of “10.0.1.2”.

At block 518, the indexer stores the events with an associated timestampin a data store 208. Timestamps enable a user to search for events basedon a time range. In some implementations, the stored events areorganized into “buckets,” where each bucket stores events associatedwith a specific time range based on the timestamps associated with eachevent. This improves time-based searching, as well as allows for eventswith recent timestamps, which may have a higher likelihood of beingaccessed, to be stored in a faster memory to facilitate fasterretrieval. For example, buckets containing the most recent events can bestored in flash memory rather than on a hard disk. In someimplementations, each bucket may be associated with an identifier, atime range, and a size constraint.

Each indexer 206 may be responsible for storing and searching a subsetof the events contained in a corresponding data store 208. Bydistributing events among the indexers and data stores, the indexers cananalyze events for a query in parallel. For example, using map-reducetechniques, each indexer returns partial responses for a subset ofevents to a search head that combines the results to produce an answerfor the query. By storing events in buckets for specific time ranges, anindexer may further optimize the data retrieval process by searchingbuckets corresponding to time ranges that are relevant to a query.

In some implementations, each indexer has a home directory and a colddirectory. The home directory of an indexer stores hot buckets and warmbuckets, and the cold directory of an indexer stores cold buckets. A hotbucket is a bucket that is capable of receiving and storing events. Awarm bucket is a bucket that can no longer receive events for storagebut has not yet been moved to the cold directory. A cold bucket is abucket that can no longer receive events and may be a bucket that waspreviously stored in the home directory. The home directory may bestored in faster memory, such as flash memory, as events may be activelywritten to the home directory, and the home directory may typicallystore events that are more frequently searched and thus are accessedmore frequently. The cold directory may be stored in slower and/orlarger memory, such as a hard disk, as events are no longer beingwritten to the cold directory, and the cold directory may typicallystore events that are not as frequently searched and thus are accessedless frequently. In some implementations, an indexer may also have aquarantine bucket that contains events having potentially inaccurateinformation, such as an incorrect time stamp associated with the eventor a time stamp that appears to be an unreasonable time stamp for thecorresponding event. The quarantine bucket may have events from any timerange; as such, the quarantine bucket may always be searched at searchtime. Additionally, an indexer may store old, archived data in a frozenbucket that is not capable of being searched at search time. In someimplementations, a frozen bucket may be stored in slower and/or largermemory, such as a hard disk, and may be stored in offline and/or remotestorage.

Moreover, events and buckets can also be replicated across differentindexers and data stores to facilitate high availability and disasterrecovery as described in U.S. Pat. No. 9,130,971, entitled “Site-BasedSearch Affinity”, issued on 8 Sep. 2015, and in U.S. patent Ser. No.14/266,817, entitled “Multi-Site Clustering”, issued on 1 Sep. 2015,each of which is hereby incorporated by reference in its entirety forall purposes.

FIG. 5B is a block diagram of an example data store 501 that includes adirectory for each index (or partition) that contains a portion of datamanaged by an indexer. FIG. 5B further illustrates details of animplementation of an inverted index 507B and an event reference array515 associated with inverted index 507B.

The data store 501 can correspond to a data store 208 that stores eventsmanaged by an indexer 206 or can correspond to a different data storeassociated with an indexer 206. In the illustrated implementation, thedata store 501 includes a _main directory 503 associated with a _mainindex and a _test directory 505 associated with a _test index. However,the data store 501 can include fewer or more directories. In someimplementations, multiple indexes can share a single directory or allindexes can share a common directory. Additionally, although illustratedas a single data store 501, it will be understood that the data store501 can be implemented as multiple data stores storing differentportions of the information shown in FIG. 5B. For example, a singleindex or partition can span multiple directories or multiple datastores, and can be indexed or searched by multiple correspondingindexers.

In the illustrated implementation of FIG. 5B, the index-specificdirectories 503 and 505 include inverted indexes 507A, 507B and 509A,509B, respectively. The inverted indexes 507A . . . 507B, and 509A . . .509B can be keyword indexes or field-value pair indexes described hereinand can include less or more information that depicted in FIG. 5B.

In some implementations, the inverted index 507A . . . 507B, and 509A .. . 509B can correspond to a distinct time-series bucket that is managedby the indexer 206 and that contains events corresponding to therelevant index (e.g., _main index, _test index). As such, each invertedindex can correspond to a particular range of time for an index.Additional files, such as high performance indexes for each time-seriesbucket of an index, can also be stored in the same directory as theinverted indexes 507A . . . 507B, and 509A . . . 509B. In someimplementations inverted index 507A . . . 507B, and 509A . . . 509B cancorrespond to multiple time-series buckets or inverted indexes 507A . .. 507B, and 509A . . . 509B can correspond to a single time-seriesbucket.

Each inverted index 507A . . . 507B, and 509A . . . 509B can include oneor more entries, such as keyword (or token) entries or field-value pairentries. Furthermore, in certain implementations, the inverted indexes507A . . . 507B, and 509A . . . 509B can include additional information,such as a time range 523 associated with the inverted index or an indexidentifier 525 identifying the index associated with the inverted index507A . . . 507B, and 509A . . . 509B. However, each inverted index 507A. . . 507B, and 509A . . . 509B can include less or more informationthan depicted.

Token entries, such as token entries 511 illustrated in inverted index507B, can include a token 511A (e.g., “error,” “itemID,” etc.) and eventreferences 511B indicative of events that include the token. Forexample, for the token “error,” the corresponding token entry includesthe token “error” and an event reference, or unique identifier, for eachevent stored in the corresponding time-series bucket that includes thetoken “error.” In the illustrated implementation of FIG. 5B, the errortoken entry includes the identifiers 3, 5, 6, 8, 11, and 12corresponding to events managed by the indexer 206 and associated withthe index _main 503 that are located in the time-series bucketassociated with the inverted index 507B.

In some cases, some token entries can be default entries, automaticallydetermined entries, or user specified entries. In some implementations,the indexer 206 can identify each word or string in an event as adistinct token and generate a token entry for it. In some cases, theindexer 206 can identify the beginning and ending of tokens based onpunctuation, spaces, as described in greater detail herein. In certaincases, the indexer 206 can rely on user input or a configuration file toidentify tokens for token entries 511, etc. It will be understood thatany combination of token entries can be included as a default,automatically determined, or included based on user-specified criteria.

Similarly, field-value pair entries, such as field-value pair entries513 shown in inverted index 507B, can include a field-value pair 513Aand event references 513B indicative of events that include a fieldvalue that corresponds to the field-value pair. For example, for afield-value pair sourcetype::sendmail, a field-value pair entry wouldinclude the field-value pair sourcetype::sendmail and a uniqueidentifier, or event reference, for each event stored in thecorresponding time-series bucket that includes a sendmail sourcetype.

In some cases, the field-value pair entries 513 can be default entries,automatically determined entries, or user specified entries. As anon-limiting example, the field-value pair entries for the fields host,source, sourcetype can be included in the inverted indexes 507A . . .507B, and 509A . . . 509B as a default. As such, all of the invertedindexes 507A . . . 507B, and 509A . . . 509B can include field-valuepair entries for the fields host, source, sourcetype. As yet anothernon-limiting example, the field-value pair entries for the IP_addressfield can be user specified and may only appear in the inverted index507B based on user-specified criteria. As another non-limiting example,as the indexer indexes the events, it can automatically identifyfield-value pairs and create field-value pair entries. For example,based on the indexers review of events, it can identify IP_address as afield in each event and add the IP_address field-value pair entries tothe inverted index 507B. It will be understood that any combination offield-value pair entries can be included as a default, automaticallydetermined, or included based on user-specified criteria.

Each unique identifier 517, or event reference, can correspond to aunique event located in the time series bucket. However, the same eventreference can be located in multiple entries. For example if an eventhas a sourcetype splunkd, host www1 and token “warning,” then the uniqueidentifier for the event will appear in the field-value pair entriessourcetype::splunkd and host::www1, as well as the token entry“warning.” With reference to the illustrated implementation of FIG. 5Band the event that corresponds to the event reference 3, the eventreference 3 is found in the field-value pair entries 513 host::hostA,source::sourceB, sourcetype::sourcetypeA, and IP_address::91.205.189.15indicating that the event corresponding to the event references is fromhostA, sourceB, of sourcetypeA, and includes 91.205.189.15 in the eventdata.

For some fields, the unique identifier is located in only onefield-value pair entry for a particular field. For example, the invertedindex may include four sourcetype field-value pair entries correspondingto four different sourcetypes of the events stored in a bucket (e.g.,sourcetypes: sendmail, splunkd, web_access, and web_service). Withinthose four sourcetype field-value pair entries, an identifier for aparticular event may appear in only one of the field-value pair entries.With continued reference to the example illustrated implementation ofFIG. 5B, since the event reference 7 appears in the field-value pairentry sourcetype::sourcetypeA, then it does not appear in the otherfield-value pair entries for the sourcetype field, includingsourcetype::sourcetypeB, sourcetype::sourcetypeC, andsourcetype::sourcetypeD.

The event references 517 can be used to locate the events in thecorresponding bucket. For example, the inverted index can include, or beassociated with, an event reference array 515. The event reference array515 can include an array entry 517 for each event reference in theinverted index 507B. Each array entry 517 can include locationinformation 519 of the event corresponding to the unique identifier(non-limiting example: seek address of the event), a timestamp 521associated with the event, or additional information regarding the eventassociated with the event reference, etc.

For each token entry 511 or field-value pair entry 513, the eventreference 501B or unique identifiers can be listed in chronologicalorder or the value of the event reference can be assigned based onchronological data, such as a timestamp associated with the eventreferenced by the event reference. For example, the event reference 1 inthe illustrated implementation of FIG. 5B can correspond to thefirst-in-time event for the bucket, and the event reference 12 cancorrespond to the last-in-time event for the bucket. However, the eventreferences can be listed in any order, such as reverse chronologicalorder, ascending order, descending order, or some other order, etc.Further, the entries can be sorted. For example, the entries can besorted alphabetically (collectively or within a particular group), byentry origin (e.g., default, automatically generated, user-specified,etc.), by entry type (e.g., field-value pair entry, token entry, etc.),or chronologically by when added to the inverted index, etc. In theillustrated implementation of FIG. 5B, the entries are sorted first byentry type and then alphabetically.

As a non-limiting example of how the inverted indexes 507A . . . 507B,and 509A . . . 509B can be used during a data categorization requestcommand, the indexers can receive filter criteria indicating data thatis to be categorized and categorization criteria indicating how the datais to be categorized. Example filter criteria can include, but is notlimited to, indexes (or partitions), hosts, sources, sourcetypes, timeranges, field identifier, keywords, etc.

Using the filter criteria, the indexer identifies relevant invertedindexes to be searched. For example, if the filter criteria includes aset of partitions, the indexer can identify the inverted indexes storedin the directory corresponding to the particular partition as relevantinverted indexes. Other means can be used to identify inverted indexesassociated with a partition of interest. For example, in someimplementations, the indexer can review an entry in the invertedindexes, such as an index-value pair entry 513 to determine if aparticular inverted index is relevant. If the filter criteria does notidentify any partition, then the indexer can identify all invertedindexes managed by the indexer as relevant inverted indexes.

Similarly, if the filter criteria includes a time range, the indexer canidentify inverted indexes corresponding to buckets that satisfy at leasta portion of the time range as relevant inverted indexes. For example,if the time range is last hour then the indexer can identify allinverted indexes that correspond to buckets storing events associatedwith timestamps within the last hour as relevant inverted indexes.

When used in combination, an index filter criterion specifying one ormore partitions and a time range filter criterion specifying aparticular time range can be used to identify a subset of invertedindexes within a particular directory (or otherwise associated with aparticular partition) as relevant inverted indexes. As such, the indexercan focus the processing to only a subset of the total number ofinverted indexes that the indexer manages.

Once the relevant inverted indexes are identified, the indexer canreview them using any additional filter criteria to identify events thatsatisfy the filter criteria. In some cases, using the known location ofthe directory in which the relevant inverted indexes are located, theindexer can determine that any events identified using the relevantinverted indexes satisfy an index filter criterion. For example, if thefilter criteria includes a partition main, then the indexer candetermine that any events identified using inverted indexes within thepartition main directory (or otherwise associated with the partitionmain) satisfy the index filter criterion.

Furthermore, based on the time range associated with each invertedindex, the indexer can determine that that any events identified using aparticular inverted index satisfies a time range filter criterion. Forexample, if a time range filter criterion is for the last hour and aparticular inverted index corresponds to events within a time range of50 minutes ago to 35 minutes ago, the indexer can determine that anyevents identified using the particular inverted index satisfy the timerange filter criterion. Conversely, if the particular inverted indexcorresponds to events within a time range of 59 minutes ago to 62minutes ago, the indexer can determine that some events identified usingthe particular inverted index may not satisfy the time range filtercriterion.

Using the inverted indexes, the indexer can identify event references(and therefore events) that satisfy the filter criteria. For example, ifthe token “error” is a filter criterion, the indexer can track all eventreferences within the token entry “error.” Similarly, the indexer canidentify other event references located in other token entries orfield-value pair entries that match the filter criteria. The system canidentify event references located in all of the entries identified bythe filter criteria. For example, if the filter criteria include thetoken “error” and field-value pair sourcetype::web_ui, the indexer cantrack the event references found in both the token entry “error” and thefield-value pair entry sourcetype::web_ui. As mentioned previously, insome cases, such as when multiple values are identified for a particularfilter criterion (e.g., multiple sources for a source filter criterion),the system can identify event references located in at least one of theentries corresponding to the multiple values and in all other entriesidentified by the filter criteria. The indexer can determine that theevents associated with the identified event references satisfy thefilter criteria.

In some cases, the indexer can further consult a timestamp associatedwith the event reference to determine whether an event satisfies thefilter criteria. For example, if an inverted index corresponds to a timerange that is partially outside of a time range filter criterion, thenthe indexer can consult a timestamp associated with the event referenceto determine whether the corresponding event satisfies the time rangecriterion. In some implementations, to identify events that satisfy atime range, the indexer can review an array, such as the event referencearray 1614 that identifies the time associated with the events.Furthermore, as mentioned above using the known location of thedirectory in which the relevant inverted indexes are located (or otherindex identifier), the indexer can determine that any events identifiedusing the relevant inverted indexes satisfy the index filter criterion.

In some cases, based on the filter criteria, the indexer reviews anextraction rule. In certain implementations, if the filter criteriaincludes a field name that does not correspond to a field-value pairentry in an inverted index, the indexer can review an extraction rule,which may be located in a configuration file, to identify a field thatcorresponds to a field-value pair entry in the inverted index.

For example, the filter criteria includes a field name “sessionID” andthe indexer determines that at least one relevant inverted index doesnot include a field-value pair entry corresponding to the field namesessionID, the indexer can review an extraction rule that identifies howthe sessionID field is to be extracted from a particular host, source,or sourcetype (implicitly identifying the particular host, source, orsourcetype that includes a sessionID field). The indexer can replace thefield name “sessionID” in the filter criteria with the identified host,source, or sourcetype. In some cases, the field name “sessionID” may beassociated with multiples hosts, sources, or sourcetypes, in which case,all identified hosts, sources, and sourcetypes can be added as filtercriteria. In some cases, the identified host, source, or sourcetype canreplace or be appended to a filter criterion, or be excluded. Forexample, if the filter criteria includes a criterion for source S1 andthe “sessionID” field is found in source S2, the source S2 can replaceS1 in the filter criteria, be appended such that the filter criteriaincludes source S1 and source S2, or be excluded based on the presenceof the filter criterion source S1. If the identified host, source, orsourcetype is included in the filter criteria, the indexer can thenidentify a field-value pair entry in the inverted index that includes afield value corresponding to the identity of the particular host,source, or sourcetype identified using the extraction rule.

Once the events that satisfy the filter criteria are identified, thesystem, such as the indexer 206 can categorize the results based on thecategorization criteria. The categorization criteria can includecategories for grouping the results, such as any combination ofpartition, source, sourcetype, or host, or other categories or fields asdesired.

The indexer can use the categorization criteria to identifycategorization criteria-value pairs or categorization criteria values bywhich to categorize or group the results. The categorizationcriteria-value pairs can correspond to one or more field-value pairentries stored in a relevant inverted index, one or more index-valuepairs based on a directory in which the inverted index is located or anentry in the inverted index (or other means by which an inverted indexcan be associated with a partition), or other criteria-value pair thatidentifies a general category and a particular value for that category.The categorization criteria values can correspond to the value portionof the categorization criteria-value pair.

As mentioned, in some cases, the categorization criteria-value pairs cancorrespond to one or more field-value pair entries stored in therelevant inverted indexes. For example, the categorizationcriteria-value pairs can correspond to field-value pair entries of host,source, and sourcetype (or other field-value pair entry as desired). Forinstance, if there are ten different hosts, four different sources, andfive different sourcetypes for an inverted index, then the invertedindex can include ten host field-value pair entries, four sourcefield-value pair entries, and five sourcetype field-value pair entries.The indexer can use the nineteen distinct field-value pair entries ascategorization criteria-value pairs to group the results.

Specifically, the indexer can identify the location of the eventreferences associated with the events that satisfy the filter criteriawithin the field-value pairs, and group the event references based ontheir location. As such, the indexer can identify the particular fieldvalue associated with the event corresponding to the event reference.For example, if the categorization criteria include host and sourcetype,the host field-value pair entries and sourcetype field-value pairentries can be used as categorization criteria-value pairs to identifythe specific host and sourcetype associated with the events that satisfythe filter criteria.

In addition, as mentioned, categorization criteria-value pairs cancorrespond to data other than the field-value pair entries in therelevant inverted indexes. For example, if partition or index is used asa categorization criterion, the inverted indexes may not includepartition field-value pair entries. Rather, the indexer can identify thecategorization criteria-value pair associated with the partition basedon the directory in which an inverted index is located, information inthe inverted index, or other information that associates the invertedindex with the partition, etc. As such a variety of methods can be usedto identify the categorization criteria-value pairs from thecategorization criteria.

Accordingly based on the categorization criteria (and categorizationcriteria-value pairs), the indexer can generate groupings based on theevents that satisfy the filter criteria. As a non-limiting example, ifthe categorization criteria includes a partition and sourcetype, thenthe groupings can correspond to events that are associated with eachunique combination of partition and sourcetype. For instance, if thereare three different partitions and two different sourcetypes associatedwith the identified events, then the six different groups can be formed,each with a unique partition value-sourcetype value combination.Similarly, if the categorization criteria includes partition,sourcetype, and host and there are two different partitions, threesourcetypes, and five hosts associated with the identified events, thenthe indexer can generate up to thirty groups for the results thatsatisfy the filter criteria. Each group can be associated with a uniquecombination of categorization criteria-value pairs (e.g., uniquecombinations of partition value sourcetype value, and host value).

In addition, the indexer can count the number of events associated witheach group based on the number of events that meet the uniquecombination of categorization criteria for a particular group (or matchthe categorization criteria-value pairs for the particular group). Withcontinued reference to the example above, the indexer can count thenumber of events that meet the unique combination of partition,sourcetype, and host for a particular group.

Each indexer communicates the groupings to the search head. The searchhead can aggregate the groupings from the indexers and provide thegroupings for display. In some cases, the groups are displayed based onat least one of the host, source, sourcetype, or partition associatedwith the groupings. In some implementations, the search head can furtherdisplay the groups based on display criteria, such as a display order ora sort order as described in greater detail above.

As a non-limiting example and with reference to FIG. 5B, consider arequest received by an indexer 206 that includes the following filtercriteria: keyword=error, partition=_main, time range=3/1/1716:22.00.000-16:28.00.000, sourcetype=sourcetypeC, host=hostB, and thefollowing categorization criteria: source.

Based on the above criteria, the indexer 206 identifies _main directory503 and can ignore _test directory 505 and any other partition-specificdirectories. The indexer determines that inverted partition 507B is arelevant partition based on its location within the _main directory 503and the time range associated with it. For sake of simplicity in thisexample, the indexer 206 determines that no other inverted indexes inthe _main directory 503, such as inverted index 507A satisfy the timerange criterion.

Having identified the relevant inverted index 507B, the indexer reviewsthe token entries 511 and the field-value pair entries 513 to identifyevent references, or events, that satisfy all of the filter criteria.

With respect to the token entries 511, the indexer can review the errortoken entry and identify event references 3, 5, 6, 8, 11, 12, indicatingthat the term “error” is found in the corresponding events. Similarly,the indexer can identify event references 4, 5, 6, 8, 9, 10, 11 in thefield-value pair entry sourcetype::sourcetypeC and event references 2,5, 6, 8, 10, 11 in the field-value pair entry host::hostB. As the filtercriteria did not include a source or an IP_address field-value pair, theindexer can ignore those field-value pair entries.

In addition to identifying event references found in at least one tokenentry or field-value pair entry (e.g., event references 3, 4, 5, 6, 8,9, 10, 11, 12), the indexer can identify events (and corresponding eventreferences) that satisfy the time range criterion using the eventreference array 1614 (e.g., event references 2, 3, 4, 5, 6, 7, 8, 9,10). Using the information obtained from the inverted index 507B(including the event reference array 515), the indexer 206 can identifythe event references that satisfy all of the filter criteria (e.g.,event references 5, 6, 8).

Having identified the events (and event references) that satisfy all ofthe filter criteria, the indexer 206 can group the event referencesusing the received categorization criteria (source). In doing so, theindexer can determine that event references 5 and 6 are located in thefield-value pair entry source::sourceD (or have matching categorizationcriteria-value pairs) and event reference 8 is located in thefield-value pair entry source::sourceC. Accordingly, the indexer cangenerate a sourceC group having a count of one corresponding toreference 8 and a sourceD group having a count of two corresponding toreferences 5 and 6. This information can be communicated to the searchhead. In turn the search head can aggregate the results from the variousindexers and display the groupings. As mentioned above, in someimplementations, the groupings can be displayed based at least in parton the categorization criteria, including at least one of host, source,sourcetype, or partition.

It will be understood that a change to any of the filter criteria orcategorization criteria can result in different groupings. As a onenon-limiting example, a request received by an indexer 206 that includesthe following filter criteria: partition=_main, time range=3/1/17 3/1/1716:21:20.000-16:28:17.000, and the following categorization criteria:host, source, sourcetype would result in the indexer identifying eventreferences 1-12 as satisfying the filter criteria. The indexer wouldthen generate up to 24 groupings corresponding to the 24 differentcombinations of the categorization criteria-value pairs, including host(hostA, hostB), source (sourceA, sourceB, sourceC, sourceD), andsourcetype (sourcetypeA, sourcetypeB, sourcetypeC). However, as thereare only twelve events identifiers in the illustrated implementation andsome fall into the same grouping, the indexer generates eight groups andcounts as follows:

Group 1 (hostA, sourceA, sourcetypeA): 1 (event reference 7)

Group 2 (hostA, sourceA, sourcetypeB): 2 (event references 1, 12)

Group 3 (hostA, sourceA, sourcetypeC): 1 (event reference 4)

Group 4 (hostA, sourceB, sourcetypeA): 1 (event reference 3)

Group 5 (hostA, sourceB, sourcetypeC): 1 (event reference 9)

Group 6 (hostB, sourceC, sourcetypeA): 1 (event reference 2)

Group 7 (hostB, sourceC, sourcetypeC): 2 (event references 8, 11)

Group 8 (hostB, sourceD, sourcetypeC): 3 (event references 5, 6, 10)

As noted, each group has a unique combination of categorizationcriteria-value pairs or categorization criteria values. The indexercommunicates the groups to the search head for aggregation with resultsreceived from other indexers. In communicating the groups to the searchhead, the indexer can include the categorization criteria-value pairsfor each group and the count. In some implementations, the indexer caninclude more or less information. For example, the indexer can includethe event references associated with each group and other identifyinginformation, such as the indexer or inverted index used to identify thegroups.

As another non-limiting examples, a request received by an indexer 206that includes the following filter criteria: partition=_main, timerange=3/1/17 3/1/17 16:21:20.000-16:28:17.000, source=sourceA, sourceD,and keyword=itemID and the following categorization criteria: host,source, sourcetype would result in the indexer identifying eventreferences 4, 7, and 10 as satisfying the filter criteria, and generatethe following groups:

Group 1 (hostA, sourceA, sourcetypeC): 1 (event reference 4)

Group 2 (hostA, sourceA, sourcetypeA): 1 (event reference 7)

Group 3 (hostB, sourceD, sourcetypeC): 1 (event references 10)

The indexer communicates the groups to the search head for aggregationwith results received from other indexers. As will be understand thereare myriad ways for filtering and categorizing the events and eventreferences. For example, the indexer can review multiple invertedindexes associated with a partition or review the inverted indexes ofmultiple partitions, and categorize the data using any one or anycombination of partition, host, source, sourcetype, or other category,as desired.

Further, if a user interacts with a particular group, the indexer canprovide additional information regarding the group. For example, theindexer can perform a targeted search or sampling of the events thatsatisfy the filter criteria and the categorization criteria for theselected group, also referred to as the filter criteria corresponding tothe group or filter criteria associated with the group.

In some cases, to provide the additional information, the indexer relieson the inverted index. For example, the indexer can identify the eventreferences associated with the events that satisfy the filter criteriaand the categorization criteria for the selected group and then use theevent reference array 515 to access some or all of the identifiedevents. In some cases, the categorization criteria values orcategorization criteria-value pairs associated with the group becomepart of the filter criteria for the review.

With reference to FIG. 5B for instance, suppose a group is displayedwith a count of six corresponding to event references 4, 5, 6, 8, 10, 11(i.e., event references 4, 5, 6, 8, 10, 11 satisfy the filter criteriaand are associated with matching categorization criteria values orcategorization criteria-value pairs) and a user interacts with the group(e.g., selecting the group, clicking on the group, etc.). In response,the search head communicates with the indexer to provide additionalinformation regarding the group.

In some implementations, the indexer identifies the event referencesassociated with the group using the filter criteria and thecategorization criteria for the group (e.g., categorization criteriavalues or categorization criteria-value pairs unique to the group).Together, the filter criteria and the categorization criteria for thegroup can be referred to as the filter criteria associated with thegroup. Using the filter criteria associated with the group, the indexeridentifies event references 4, 5, 6, 8, 10, 11.

Based on a sampling criteria, discussed in greater detail above, theindexer can determine that it will analyze a sample of the eventsassociated with the event references 4, 5, 6, 8, 10, 11. For example,the sample can include analyzing event data associated with the eventreferences 5, 8, 10. In some implementations, the indexer can use theevent reference array 1616 to access the event data associated with theevent references 5, 8, 10. Once accessed, the indexer can compile therelevant information and provide it to the search head for aggregationwith results from other indexers. By identifying events and samplingevent data using the inverted indexes, the indexer can reduce the amountof actual data this is analyzed and the number of events that areaccessed in order to generate the summary of the group and provide aresponse in less time.

2.8 Query Processing

FIG. 6A is a flow diagram of an example method that illustrates how asearch head and indexers perform a search query, in accordance withexample implementations. At block 602, a search head receives a searchquery from a client. At block 604, the search head analyzes the searchquery to determine what portion(s) of the query can be delegated toindexers and what portions of the query can be executed locally by thesearch head. At block 606, the search head distributes the determinedportions of the query to the appropriate indexers. In someimplementations, a search head cluster may take the place of anindependent search head where each search head in the search headcluster coordinates with peer search heads in the search head cluster toschedule jobs, replicate search results, update configurations, fulfillsearch requests, etc. In some implementations, the search head (or eachsearch head) communicates with a master node (also known as a clustermaster, not shown in FIG. 2 ) that provides the search head with a listof indexers to which the search head can distribute the determinedportions of the query. The master node maintains a list of activeindexers and can also designate which indexers may have responsibilityfor responding to queries over certain sets of events. A search head maycommunicate with the master node before the search head distributesqueries to indexers to discover the addresses of active indexers.

At block 608, the indexers to which the query was distributed, searchdata stores associated with them for events that are responsive to thequery. To determine which events are responsive to the query, theindexer searches for events that match the criteria specified in thequery. These criteria can include matching keywords or specific valuesfor certain fields. The searching operations at block 608 may use thelate-binding schema to extract values for specified fields from eventsat the time the query is processed. In some implementations, one or morerules for extracting field values may be specified as part of a sourcetype definition in a configuration file. The indexers may then eithersend the relevant events back to the search head, or use the events todetermine a partial result, and send the partial result back to thesearch head.

At block 610, the search head combines the partial results and/or eventsreceived from the indexers to produce a final result for the query. Insome examples, the results of the query are indicative of performance orsecurity of the IT environment and may help improve the performance ofcomponents in the IT environment. This final result may comprisedifferent types of data depending on what the query requested. Forexample, the results can include a listing of matching events returnedby the query, or some type of visualization of the data from thereturned events. In another example, the final result can include one ormore calculated values derived from the matching events.

The results generated by the system 108 can be returned to a clientusing different techniques. For example, one technique streams resultsor relevant events back to a client in real-time as they are identified.Another technique waits to report the results to the client until acomplete set of results (which may include a set of relevant events or aresult based on relevant events) is ready to return to the client. Yetanother technique streams interim results or relevant events back to theclient in real-time until a complete set of results is ready, and thenreturns the complete set of results to the client. In another technique,certain results are stored as “search jobs” and the client may retrievethe results by referring the search jobs.

The search head can also perform various operations to make the searchmore efficient. For example, before the search head begins execution ofa query, the search head can determine a time range for the query and aset of common keywords that all matching events include. The search headmay then use these parameters to query the indexers to obtain a supersetof the eventual results. Then, during a filtering stage, the search headcan perform field-extraction operations on the superset to produce areduced set of search results. This speeds up queries, which may beparticularly helpful for queries that are performed on a periodic basis.

2.9 Pipelined Search Language

Various implementations of the present disclosure can be implementedusing, or in conjunction with, a pipelined command language. A pipelinedcommand language is a language in which a set of inputs or data isoperated on by a first command in a sequence of commands, and thensubsequent commands in the order they are arranged in the sequence. Suchcommands can include any type of functionality for operating on data,such as retrieving, searching, filtering, aggregating, processing,transmitting, and the like. As described herein, a query can thus beformulated in a pipelined command language and include any number ofordered or unordered commands for operating on data.

Splunk Processing Language (SPL) is an example of a pipelined commandlanguage in which a set of inputs or data is operated on by any numberof commands in a particular sequence. A sequence of commands, or commandsequence, can be formulated such that the order in which the commandsare arranged defines the order in which the commands are applied to aset of data or the results of an earlier executed command. For example,a first command in a command sequence can operate to search or filterfor specific data in particular set of data. The results of the firstcommand can then be passed to another command listed later in thecommand sequence for further processing.

In various implementations, a query can be formulated as a commandsequence defined in a command line of a search UI. In someimplementations, a query can be formulated as a sequence of SPLcommands. Some or all of the SPL commands in the sequence of SPLcommands can be separated from one another by a pipe symbol “|”. In suchimplementations, a set of data, such as a set of events, can be operatedon by a first SPL command in the sequence, and then a subsequent SPLcommand following a pipe symbol “|” after the first SPL command operateson the results produced by the first SPL command or other set of data,and so on for any additional SPL commands in the sequence. As such, aquery formulated using SPL comprises a series of consecutive commandsthat are delimited by pipe “|” characters. The pipe character indicatesto the system that the output or result of one command (to the left ofthe pipe) should be used as the input for one of the subsequent commands(to the right of the pipe). This enables formulation of queries definedby a pipeline of sequenced commands that refines or enhances the data ateach step along the pipeline until the desired results are attained.Accordingly, various implementations described herein can be implementedwith Splunk Processing Language (SPL) used in conjunction with theSPLUNK® ENTERPRISE system.

While a query can be formulated in many ways, a query can start with asearch command and one or more corresponding search terms at thebeginning of the pipeline. Such search terms can include any combinationof keywords, phrases, times, dates, Boolean expressions, fieldname-fieldvalue pairs, etc. that specify which results should be obtained from anindex. The results can then be passed as inputs into subsequent commandsin a sequence of commands by using, for example, a pipe character. Thesubsequent commands in a sequence can include directives for additionalprocessing of the results once it has been obtained from one or moreindexes. For example, commands may be used to filter unwantedinformation out of the results, extract more information, evaluate fieldvalues, calculate statistics, reorder the results, create an alert,create summary of the results, or perform some type of aggregationfunction. In some implementations, the summary can include a graph,chart, metric, or other visualization of the data. An aggregationfunction can include analysis or calculations to return an aggregatevalue, such as an average value, a sum, a maximum value, a root meansquare, statistical values, and the like.

Due to its flexible nature, use of a pipelined command language invarious implementations is advantageous because it can perform“filtering” as well as “processing” functions. In other words, a singlequery can include a search command and search term expressions, as wellas data-analysis expressions. For example, a command at the beginning ofa query can perform a “filtering” step by retrieving a set of data basedon a condition (e.g., records associated with server response times ofless than 1 microsecond). The results of the filtering step can then bepassed to a subsequent command in the pipeline that performs a“processing” step (e.g. calculation of an aggregate value related to thefiltered events such as the average response time of servers withresponse times of less than 1 microsecond). Furthermore, the searchcommand can allow events to be filtered by keyword as well as fieldvalue criteria. For example, a search command can filter out all eventscontaining the word “warning” or filter out all events where a fieldvalue associated with a field “clientip” is “10.0.1.2.”

The results obtained or generated in response to a command in a querycan be considered a set of results data. The set of results data can bepassed from one command to another in any data format. In oneimplementation, the set of result data can be in the form of adynamically created table. Each command in a particular query canredefine the shape of the table. In some implementations, an eventretrieved from an index in response to a query can be considered a rowwith a column for each field value. Columns contain basic informationabout the data and also may contain data that has been dynamicallyextracted at search time.

FIG. 6B provides a visual representation of the manner in which apipelined command language or query operates in accordance with thedisclosed implementations. The query 630 can be inputted by the userinto a search. The query comprises a search, the results of which arepiped to two commands (namely, command 1 and command 2) that follow thesearch step.

Disk 622 represents the event data in the raw record data store.

When a user query is processed, a search step will precede other queriesin the pipeline in order to generate a set of events at block 640. Forexample, the query can comprise search terms “sourcetype=syslog ERROR”at the front of the pipeline as shown in FIG. 6B. Intermediate resultstable 624 shows fewer rows because it represents the subset of eventsretrieved from the index that matched the search terms“sourcetype=syslog ERROR” from search command 630. By way of furtherexample, instead of a search step, the set of events at the head of thepipeline may be generating by a call to a pre-existing inverted index(as will be explained later).

At block 642, the set of events generated in the first part of the querymay be piped to a query that searches the set of events for field-valuepairs or for keywords. For example, the second intermediate resultstable 626 shows fewer columns, representing the result of the topcommand, “top user” which may summarize the events into a list of thetop 10 users and may display the user, count, and percentage.

Finally, at block 644, the results of the prior stage can be pipelinedto another stage where further filtering or processing of the data canbe performed, e.g., preparing the data for display purposes, filteringthe data based on a condition, performing a mathematical calculationwith the data, etc. As shown in FIG. 6B, the “fields—percent” part ofcommand 630 removes the column that shows the percentage, thereby,leaving a final results table 628 without a percentage column. Indifferent implementations, other query languages, such as the StructuredQuery Language (“SQL”), can be used to create a query.

2.10 Field Extraction

The search head 210 allows users to search and visualize eventsgenerated from machine data received from homogenous data sources. Thesearch head 210 also allows users to search and visualize eventsgenerated from machine data received from heterogeneous data sources.The search head 210 includes various mechanisms, which may additionallyreside in an indexer 206, for processing a query. A query language maybe used to create a query, such as any suitable pipelined querylanguage. For example, Splunk Processing Language (SPL) can be utilizedto make a query. SPL is a pipelined search language in which a set ofinputs is operated on by a first command in a command line, and then asubsequent command following the pipe symbol “|” operates on the resultsproduced by the first command, and so on for additional commands. Otherquery languages, such as the Structured Query Language (“SQL”), can beused to create a query.

In response to receiving the search query, search head 210 usesextraction rules to extract values for fields in the events beingsearched. The search head 210 obtains extraction rules that specify howto extract a value for fields from an event. Extraction rules cancomprise regex rules that specify how to extract values for the fieldscorresponding to the extraction rules. In addition to specifying how toextract field values, the extraction rules may also include instructionsfor deriving a field value by performing a function on a characterstring or value retrieved by the extraction rule. For example, anextraction rule may truncate a character string or convert the characterstring into a different data format. In some cases, the query itself canspecify one or more extraction rules.

The search head 210 can apply the extraction rules to events that itreceives from indexers 206. Indexers 206 may apply the extraction rulesto events in an associated data store 208. Extraction rules can beapplied to all the events in a data store or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the portions of machine datain the events and examining the data for one or more patterns ofcharacters, numbers, delimiters, etc., that indicate where the fieldbegins and, optionally, ends.

FIG. 7A is a diagram of an example scenario where a common customeridentifier is found among log data received from three disparate datasources, in accordance with example implementations. In this example, auser submits an order for merchandise using a vendor's shoppingapplication program 701 running on the user's system. In this example,the order was not delivered to the vendor's server due to a resourceexception at the destination server that is detected by the middlewarecode 702. The user then sends a message to the customer support server703 to complain about the order failing to complete. The three systems701, 702, and 703 are disparate systems that do not have a commonlogging format. The order application 701 sends log data 704 to the dataintake and query system in one format, the middleware code 702 sendserror log data 705 in a second format, and the support server 703 sendslog data 706 in a third format.

Using the log data received at one or more indexers 206 from the threesystems, the vendor can uniquely obtain an insight into user activity,user experience, and system behavior. The search head 210 allows thevendor's administrator to search the log data from the three systemsthat one or more indexers 206 are responsible for searching, therebyobtaining correlated information, such as the order number andcorresponding customer ID number of the person placing the order. Thesystem also allows the administrator to see a visualization of relatedevents via a user interface. The administrator can query the search head210 for customer ID field value matches across the log data from thethree systems that are stored at the one or more indexers 206. Thecustomer ID field value exists in the data gathered from the threesystems, but the customer ID field value may be located in differentareas of the data given differences in the architecture of the systems.There is a semantic relationship between the customer ID field valuesgenerated by the three systems. The search head 210 requests events fromthe one or more indexers 206 to gather relevant events from the threesystems. The search head 210 then applies extraction rules to the eventsin order to extract field values that it can correlate. The search headmay apply a different extraction rule to each set of events from eachsystem when the event format differs among systems. In this example, theuser interface can display to the administrator the events correspondingto the common customer ID field values 707, 708, and 709, therebyproviding the administrator with insight into a customer's experience.

Note that query results can be returned to a client, a search head, orany other system component for further processing. In general, queryresults may include a set of one or more events, a set of one or morevalues obtained from the events, a subset of the values, statisticscalculated based on the values, a report containing the values, avisualization (e.g., a graph or chart) generated from the values, andthe like.

The search system enables users to run queries against the stored datato retrieve events that meet criteria specified in a query, such ascontaining certain keywords or having specific values in defined fields.FIG. 7B illustrates the manner in which keyword searches and fieldsearches are processed in accordance with disclosed implementations.

If a user inputs a search query into search bar 1401 that includes onlykeywords (also known as “tokens”), e.g., the keyword “error” or“warning”, the query search engine of the data intake and query systemsearches for those keywords directly in the event data 722 stored in theraw record data store. Note that while FIG. 7B only illustrates fourevents, the raw record data store (corresponding to data store 208 inFIG. 2 ) may contain records for millions of events.

As disclosed above, an indexer can optionally generate a keyword indexto facilitate fast keyword searching for event data. The indexerincludes the identified keywords in an index, which associates eachstored keyword with reference pointers to events containing that keyword(or to locations within events where that keyword is located, otherlocation identifiers, etc.). When an indexer subsequently receives akeyword-based query, the indexer can access the keyword index to quicklyidentify events containing the keyword. For example, if the keyword“HTTP” was indexed by the indexer at index time, and the user searchesfor the keyword “HTTP”, events 713 to 715 will be identified based onthe results returned from the keyword index. As noted above, the indexcontains reference pointers to the events containing the keyword, whichallows for efficient retrieval of the relevant events from the rawrecord data store.

If a user searches for a keyword that has not been indexed by theindexer, the data intake and query system would nevertheless be able toretrieve the events by searching the event data for the keyword in theraw record data store directly as shown in FIG. 7B. For example, if auser searches for the keyword “frank”, and the name “frank” has not beenindexed at index time, the DATA INTAKE AND QUERY system will search theevent data directly and return the first event 713. Note that whetherthe keyword has been indexed at index time or not, in both cases the rawdata with the events 712 is accessed from the raw data record store toservice the keyword search. In the case where the keyword has beenindexed, the index will contain a reference pointer that will allow fora more efficient retrieval of the event data from the data store. If thekeyword has not been indexed, the search engine will need to searchthrough all the records in the data store to service the search.

In most cases, however, in addition to keywords, a user's search willalso include fields. The term “field” refers to a location in the eventdata containing one or more values for a specific data item. Often, afield is a value with a fixed, delimited position on a line, or a nameand value pair, where there is a single value to each field name. Afield can also be multivalued, that is, it can appear more than once inan event and have a different value for each appearance, e.g., emailaddress fields. Fields are searchable by the field name or fieldname-value pairs. Some examples of fields are “clientip” for IPaddresses accessing a web server, or the “From” and “To” fields in emailaddresses.

By way of further example, consider the search, “status=404”. Thissearch query finds events with “status” fields that have a value of“404.” When the search is run, the search engine does not look forevents with any other “status” value. It also does not look for eventscontaining other fields that share “404” as a value. As a result, thesearch returns a set of results that are more focused than if “404” hadbeen used in the search string as part of a keyword search. Note alsothat fields can appear in events as “key=value” pairs such as“user_name=Bob.” But in most cases, field values appear in fixed,delimited positions without identifying keys. For example, the datastore may contain events where the “user_name” value always appears byitself after the timestamp as illustrated by the following string:“November 15 09:33:22 johnmedlock.”

The data intake and query system advantageously allows for search timefield extraction. In other words, fields can be extracted from the eventdata at search time using late-binding schema as opposed to at dataingestion time, which was a major limitation of the prior art systems.

In response to receiving the search query, search head 210 usesextraction rules to extract values for the fields associated with afield or fields in the event data being searched. The search head 210obtains extraction rules that specify how to extract a value for certainfields from an event. Extraction rules can comprise regex rules thatspecify how to extract values for the relevant fields. In addition tospecifying how to extract field values, the extraction rules may alsoinclude instructions for deriving a field value by performing a functionon a character string or value retrieved by the extraction rule. Forexample, a transformation rule may truncate a character string, orconvert the character string into a different data format. In somecases, the query itself can specify one or more extraction rules.

FIG. 7B illustrates the manner in which configuration files may be usedto configure custom fields at search time in accordance with thedisclosed implementations. In response to receiving a search query, thedata intake and query system determines if the query references a“field.” For example, a query may request a list of events where the“clientip” field equals “127.0.0.1.” If the query itself does notspecify an extraction rule and if the field is not a metadata field,e.g., time, host, source, source type, etc., then in order to determinean extraction rule, the search engine may, in one or moreimplementations, need to locate configuration file 712 during theexecution of the search as shown in FIG. 7B.

Configuration file 712 may contain extraction rules for all the variousfields that are not metadata fields, e.g., the “clientip” field. Theextraction rules may be inserted into the configuration file in avariety of ways. In some implementations, the extraction rules cancomprise regular expression rules that are manually entered in by theuser. Regular expressions match patterns of characters in text and areused for extracting custom fields in text.

In one or more implementations, as noted above, a field extractor may beconfigured to automatically generate extraction rules for certain fieldvalues in the events when the events are being created, indexed, orstored, or possibly at a later time. In one implementation, a user maybe able to dynamically create custom fields by highlighting portions ofa sample event that should be extracted as fields using a graphical userinterface. The system would then generate a regular expression thatextracts those fields from similar events and store the regularexpression as an extraction rule for the associated field in theconfiguration file 712.

In some implementations, the indexers may automatically discover certaincustom fields at index time and the regular expressions for those fieldswill be automatically generated at index time and stored as part ofextraction rules in configuration file 712. For example, fields thatappear in the event data as “key=value” pairs may be automaticallyextracted as part of an automatic field discovery process. Note thatthere may be several other ways of adding field definitions toconfiguration files in addition to the methods discussed herein.

The search head 210 can apply the extraction rules derived fromconfiguration file 1402 to event data that it receives from indexers206. Indexers 206 may apply the extraction rules from the configurationfile to events in an associated data store 208. Extraction rules can beapplied to all the events in a data store, or to a subset of the eventsthat have been filtered based on some criteria (e.g., event time stampvalues, etc.). Extraction rules can be used to extract one or morevalues for a field from events by parsing the event data and examiningthe event data for one or more patterns of characters, numbers,delimiters, etc., that indicate where the field begins and, optionally,ends.

In one more implementations, the extraction rule in configuration file712 will also need to define the type or set of events that the ruleapplies to. Because the raw record data store will contain events frommultiple heterogeneous sources, multiple events may contain the samefields in different locations because of discrepancies in the format ofthe data generated by the various sources. Furthermore, certain eventsmay not contain a particular field at all. For example, event 719 alsocontains “clientip” field, however, the “clientip” field is in adifferent format from events 713-715. To address the discrepancies inthe format and content of the different types of events, theconfiguration file will also need to specify the set of events that anextraction rule applies to, e.g., extraction rule 716 specifies a rulefor filtering by the type of event and contains a regular expression forparsing out the field value. Accordingly, each extraction rule willpertain to only a particular type of event. If a particular field, e.g.,“clientip” occurs in multiple events, each of those types of eventswould need its own corresponding extraction rule in the configurationfile 712 and each of the extraction rules would comprise a differentregular expression to parse out the associated field value. The mostcommon way to categorize events is by source type because eventsgenerated by a particular source can have the same format.

The field extraction rules stored in configuration file 712 performsearch-time field extractions. For example, for a query that requests alist of events with source type “access_combined” where the “clientip”field equals “127.0.0.1,” the query search engine would first locate theconfiguration file 712 to retrieve extraction rule 716 that would allowit to extract values associated with the “clientip” field from the eventdata 720 “where the source type is “access_combined. After the“clientip” field has been extracted from all the events comprising the“clientip” field where the source type is “access_combined,” the querysearch engine can then execute the field criteria by performing thecompare operation to filter out the events where the “clientip” fieldequals “127.0.0.1.” In the example shown in FIG. 7B, events 713-715would be returned in response to the user query. In this manner, thesearch engine can service queries containing field criteria in additionto queries containing keyword criteria (as explained above).

The configuration file can be created during indexing. It may either bemanually created by the user or automatically generated with certainpredetermined field extraction rules. As discussed above, the events maybe distributed across several indexers, wherein each indexer may beresponsible for storing and searching a subset of the events containedin a corresponding data store. In a distributed indexer system, eachindexer would need to maintain a local copy of the configuration filethat is synchronized periodically across the various indexers.

The ability to add schema to the configuration file at search timeresults in increased efficiency. A user can create new fields at searchtime and simply add field definitions to the configuration file. As auser learns more about the data in the events, the user can continue torefine the late-binding schema by adding new fields, deleting fields, ormodifying the field extraction rules in the configuration file for usethe next time the schema is used by the system. Because the data intakeand query system maintains the underlying raw data and uses late-bindingschema for searching the raw data, it enables a user to continueinvestigating and learn valuable insights about the raw data long afterdata ingestion time.

The ability to add multiple field definitions to the configuration fileat search time also results in increased flexibility. For example,multiple field definitions can be added to the configuration file tocapture the same field across events generated by different sourcetypes. This allows the data intake and query system to search andcorrelate data across heterogeneous sources flexibly and efficiently.

Further, by providing the field definitions for the queried fields atsearch time, the configuration file 712 allows the record data store 712to be field searchable. In other words, the raw record data store 712can be searched using keywords as well as fields, wherein the fields aresearchable name/value pairings that distinguish one event from anotherand can be defined in configuration file 1402 using extraction rules. Incomparison to a search containing field names, a keyword search does notneed the configuration file and can search the event data directly asshown in FIG. 7B.

It should also be noted that any events filtered out by performing asearch-time field extraction using a configuration file can be furtherprocessed by directing the results of the filtering step to a processingstep using a pipelined search language. Using the prior example, a usermay pipeline the results of the compare step to an aggregate function byasking the query search engine to count the number of events where the“clientip” field equals “127.0.0.1.”

2.11 Example Search Screen

FIG. 8A is an interface diagram of an example user interface for asearch screen 800, in accordance with example implementations. Searchscreen 800 includes a search bar 802 that accepts user input in the formof a search string. It also includes a time range picker 812 thatenables the user to specify a time range for the search. For historicalsearches (e.g., searches based on a particular historical time range),the user can select a specific time range, or alternatively a relativetime range, such as “today,” “yesterday” or “last week.” For real-timesearches (e.g., searches whose results are based on data received inreal-time), the user can select the size of a preceding time window tosearch for real-time events. Search screen 800 also initially maydisplay a “data summary” dialog as is illustrated in FIG. 8B thatenables the user to select different sources for the events, such as byselecting specific hosts and log files.

After the search is executed, the search screen 800 in FIG. 8A candisplay the results through search results tabs 804, wherein searchresults tabs 804 includes: an “events tab” that may display variousinformation about events returned by the search; a “statistics tab” thatmay display statistics about the search results; and a “visualizationtab” that may display various visualizations of the search results. Theevents tab illustrated in FIG. 8A may display a timeline graph 805 thatgraphically illustrates the number of events that occurred in one-hourintervals over the selected time range. The events tab also may displayan events list 808 that enables a user to view the machine data in eachof the returned events.

The events tab additionally may display a sidebar that is an interactivefield picker 806. The field picker 806 may be displayed to a user inresponse to the search being executed and allows the user to furtheranalyze the search results based on the fields in the events of thesearch results. The field picker 806 includes field names that referencefields present in the events in the search results. The field picker maydisplay any Selected Fields 820 that a user has pre-selected for display(e.g., host, source, sourcetype) and may also display any InterestingFields 822 that the system determines may be interesting to the userbased on pre-specified criteria (e.g., action, bytes, categoryid,clientip, date_hour, date_mday, date_minute, etc.). The field pickeralso provides an option to display field names for all the fieldspresent in the events of the search results using the All Fields control824.

Each field name in the field picker 806 has a value type identifier tothe left of the field name, such as value type identifier 826. A valuetype identifier identifies the type of value for the respective field,such as an “a” for fields that include literal values or a “#” forfields that include numerical values.

Each field name in the field picker also has a unique value count to theright of the field name, such as unique value count 828. The uniquevalue count indicates the number of unique values for the respectivefield in the events of the search results.

Each field name is selectable to view the events in the search resultsthat have the field referenced by that field name. For example, a usercan select the “host” field name, and the events shown in the eventslist 808 will be updated with events in the search results that have thefield that is reference by the field name “host.”

2.12 Data Models

A data model is a hierarchically structured search-time mapping ofsemantic knowledge about one or more datasets. It encodes the domainknowledge used to build a variety of specialized searches of thosedatasets. Those searches, in turn, can be used to generate reports.

A data model is composed of one or more “objects” (or “data modelobjects”) that define or otherwise correspond to a specific set of data.An object is defined by constraints and attributes. An object'sconstraints are search criteria that define the set of events to beoperated on by running a search having that search criteria at the timethe data model is selected. An object's attributes are the set of fieldsto be exposed for operating on that set of events generated by thesearch criteria.

Objects in data models can be arranged hierarchically in parent/childrelationships. Each child object represents a subset of the datasetcovered by its parent object. The top-level objects in data models arecollectively referred to as “root objects.”

Child objects have inheritance. Child objects inherit constraints andattributes from their parent objects and may have additional constraintsand attributes of their own. Child objects provide a way of filteringevents from parent objects. Because a child object may provide anadditional constraint in addition to the constraints it has inheritedfrom its parent object, the dataset it represents may be a subset of thedataset that its parent represents. For example, a first data modelobject may define a broad set of data pertaining to e-mail activitygenerally, and another data model object may define specific datasetswithin the broad dataset, such as a subset of the e-mail data pertainingspecifically to e-mails sent. For example, a user can simply select an“e-mail activity” data model object to access a dataset relating toe-mails generally (e.g., sent or received), or select an “e-mails sent”data model object (or data sub-model object) to access a datasetrelating to e-mails sent.

Because a data model object is defined by its constraints (e.g., a setof search criteria) and attributes (e.g., a set of fields), a data modelobject can be used to quickly search data to identify a set of eventsand to identify a set of fields to be associated with the set of events.For example, an “e-mails sent” data model object may specify a searchfor events relating to e-mails that have been sent, and specify a set offields that are associated with the events. Thus, a user can retrieveand use the “e-mails sent” data model object to quickly search sourcedata for events relating to sent e-mails, and may be provided with alisting of the set of fields relevant to the events in a user interfacescreen.

Examples of data models can include electronic mail, authentication,databases, intrusion detection, malware, application state, alerts,compute inventory, network sessions, network traffic, performance,audits, updates, vulnerabilities, etc. Data models and their objects canbe designed by knowledge managers in an organization, and they canenable downstream users to quickly focus on a specific set of data. Auser iteratively applies a model development tool (not shown in FIG. 8A)to prepare a query that defines a subset of events and assigns an objectname to that subset. A child subset is created by further limiting aquery that generated a parent subset.

Data definitions in associated schemas can be taken from the commoninformation model (CIM) or can be devised for a particular schema andoptionally added to the CIM. Child objects inherit fields from parentsand can include fields not present in parents. A model developer canselect fewer extraction rules than are available for the sourcesreturned by the query that defines events belonging to a model.Selecting a limited set of extraction rules can be a tool forsimplifying and focusing the data model, while allowing a userflexibility to explore the data subset. Development of a data model isfurther explained in U.S. Pat. Nos. 8,788,525 and 8,788,526, bothentitled “DATA MODEL FOR MACHINE DATA FOR SEMANTIC SEARCH”, both issuedon 22 Jul. 2014, U.S. Pat. No. 8,983,994, entitled “GENERATION OF A DATAMODEL FOR SEARCHING MACHINE DATA”, issued on 17 Mar. 2015, U.S. Pat. No.9,128,980, entitled “GENERATION OF A DATA MODEL APPLIED TO QUERIES”,issued on 8 Sep. 2015, and U.S. Pat. No. 9,589,012, entitled “GENERATIONOF A DATA MODEL APPLIED TO OBJECT QUERIES”, issued on 7 Mar. 2017, eachof which is hereby incorporated by reference in its entirety for allpurposes.

A data model can also include reports. One or more report formats can beassociated with a particular data model and be made available to runagainst the data model. A user can use child objects to design reportswith object datasets that already have extraneous data pre-filtered out.In some implementations, the data intake and query system 108 providesthe user with the ability to produce reports (e.g., a table, chart,visualization, etc.) without having to enter SPL, SQL, or other querylanguage terms into a search screen. Data models are used as the basisfor the search feature.

Data models may be selected in a report generation interface. The reportgenerator supports drag-and-drop organization of fields to be summarizedin a report. When a model is selected, the fields with availableextraction rules are made available for use in the report. The user mayrefine and/or filter search results to produce more precise reports. Theuser may select some fields for organizing the report and select otherfields for providing detail according to the report organization. Forexample, “region” and “salesperson” are fields used for organizing thereport and sales data can be summarized (subtotaled and totaled) withinthis organization. The report generator allows the user to specify oneor more fields within events and apply statistical analysis on valuesextracted from the specified one or more fields. The report generatormay aggregate search results across sets of events and generatestatistics based on aggregated search results. Building reports usingthe report generation interface is further explained in U.S. patentapplication Ser. No. 14/503,335, entitled “GENERATING REPORTS FROMUNSTRUCTURED DATA”, filed on 30 Sep. 2014, and which is herebyincorporated by reference in its entirety for all purposes. Datavisualizations also can be generated in a variety of formats, byreference to the data model. Reports, data visualizations, and datamodel objects can be saved and associated with the data model for futureuse. The data model object may be used to perform searches of otherdata.

FIGS. 9-15 are interface diagrams of example report generation userinterfaces, in accordance with example implementations. The reportgeneration process may be driven by a predefined data model object, suchas a data model object defined and/or saved via a reporting applicationor a data model object obtained from another source. A user can load asaved data model object using a report editor. For example, the initialsearch query and fields used to drive the report editor may be obtainedfrom a data model object. The data model object that is used to drive areport generation process may define a search and a set of fields. Uponloading of the data model object, the report generation process mayenable a user to use the fields (e.g., the fields defined by the datamodel object) to define criteria for a report (e.g., filters, splitrows/columns, aggregates, etc.) and the search may be used to identifyevents (e.g., to identify events responsive to the search) used togenerate the report. That is, for example, if a data model object isselected to drive a report editor, the graphical user interface of thereport editor may enable a user to define reporting criteria for thereport using the fields associated with the selected data model object,and the events used to generate the report may be constrained to theevents that match, or otherwise satisfy, the search constraints of theselected data model object.

The selection of a data model object for use in driving a reportgeneration may be facilitated by a data model object selectioninterface. FIG. 9 illustrates an example interactive data modelselection graphical user interface 900 of a report editor that maydisplay a listing of available data models 901. The user may select oneof the data models 902.

FIG. 10 illustrates an example data model object selection graphicaluser interface 1000 that may display available data objects 1001 for theselected data object model 902. The user may select one of the displayeddata model objects 1002 for use in driving the report generationprocess.

Once a data model object is selected by the user, a user interfacescreen 1100 shown in FIG. 11A may display an interactive listing ofautomatic field identification options 1101 based on the selected datamodel object. For example, a user may select one of the threeillustrated options (e.g., the “All Fields” option 1102, the “SelectedFields” option 1103, or the “Coverage” option (e.g., fields with atleast a specified % of coverage) 1104). If the user selects the “AllFields” option 1102, all of the fields identified from the events thatwere returned in response to an initial search query may be selected.That is, for example, all of the fields of the identified data modelobject fields may be selected. If the user selects the “Selected Fields”option 1103, only the fields from the fields of the identified datamodel object fields that are selected by the user may be used. If theuser selects the “Coverage” option 1104, only the fields of theidentified data model object fields meeting a specified coveragecriteria may be selected. A percent coverage may refer to the percentageof events returned by the initial search query that a given fieldappears in. Thus, for example, if an object dataset includes 10,000events returned in response to an initial search query, and the“avg_age” field appears in 854 of those 10,000 events, then the“avg_age” field would have a coverage of 8.54% for that object dataset.If, for example, the user selects the “Coverage” option and specifies acoverage value of 2%, only fields having a coverage value equal to orgreater than 2% may be selected. The number of fields corresponding toeach selectable option may be displayed in association with each option.For example, “97” displayed next to the “All Fields” option 1102indicates that 97 fields will be selected if the “All Fields” option isselected. The “3” displayed next to the “Selected Fields” option 1103indicates that 3 of the 97 fields will be selected if the “SelectedFields” option is selected. The “49” displayed next to the “Coverage”option 1104 indicates that 49 of the 97 fields (e.g., the 49 fieldshaving a coverage of 2% or greater) will be selected if the “Coverage”option is selected. The number of fields corresponding to the “Coverage”option may be dynamically updated based on the specified percent ofcoverage.

FIG. 11B illustrates an example graphical user interface screen 1105displaying the reporting application's “Report Editor” page. The screenmay display interactive elements for defining various elements of areport. For example, the page includes a “Filters” element 1106, a“Split Rows” element 1107, a “Split Columns” element 1108, and a “ColumnValues” element 1109. The page may include a list of search results1111. In this example, the Split Rows element 1107 is expanded,revealing a listing of fields 1110 that can be used to define additionalcriteria (e.g., reporting criteria). The listing of fields 1110 maycorrespond to the selected fields. That is, the listing of fields 1110may list only the fields previously selected, either automaticallyand/or manually by a user. FIG. 11C illustrates a formatting dialogue1112 that may be displayed upon selecting a field from the listing offields 1110. The dialogue can be used to format the display of theresults of the selection (e.g., label the column for the selected fieldto be displayed as “component”).

FIG. 11D illustrates an example graphical user interface screen 1105including a table of results 1113 based on the selected criteriaincluding splitting the rows by the “component” field. A column 1114having an associated count for each component listed in the table may bedisplayed that indicates an aggregate count of the number of times thatthe particular field-value pair (e.g., the value in a row for aparticular field, such as the value “BucketMover” for the field“component”) occurs in the set of events responsive to the initialsearch query.

FIG. 12 illustrates an example graphical user interface screen 1200 thatallows the user to filter search results and to perform statisticalanalysis on values extracted from specific fields in the set of events.In this example, the top ten product names ranked by price are selectedas a filter 1201 that causes the display of the ten most popularproducts sorted by price. Each row is displayed by product name andprice 1202. This results in each product displayed in a column labeled“product name” along with an associated price in a column labeled“price” 1206. Statistical analysis of other fields in the eventsassociated with the ten most popular products have been specified ascolumn values 1203. A count of the number of successful purchases foreach product is displayed in column 1204. These statistics may beproduced by filtering the search results by the product name, findingall occurrences of a successful purchase in a field within the eventsand generating a total of the number of occurrences. A sum of the totalsales is displayed in column 1205, which is a result of themultiplication of the price and the number of successful purchases foreach product.

The reporting application allows the user to create graphicalvisualizations of the statistics generated for a report. For example,FIG. 13 illustrates an example graphical user interface 1300 that maydisplay a set of components and associated statistics 1301. Thereporting application allows the user to select a visualization of thestatistics in a graph (e.g., bar chart, scatter plot, area chart, linechart, pie chart, radial gauge, marker gauge, filler gauge, etc.), wherethe format of the graph may be selected using the user interfacecontrols 1302 along the left panel of the user interface 1300. FIG. 14illustrates an example of a bar chart visualization 1400 of an aspect ofthe statistical data 1301. FIG. 15 illustrates a scatter plotvisualization 1500 of an aspect of the statistical data 1301.

2.13 Acceleration Technique

The above-described system provides significant flexibility by enablinga user to analyze massive quantities of minimally-processed data “on thefly” at search time using a late-binding schema, instead of storingpre-specified portions of the data in a database at ingestion time. Thisflexibility enables a user to see valuable insights, correlate data, andperform subsequent queries to examine interesting aspects of the datathat may not have been apparent at ingestion time.

However, performing extraction and analysis operations at search timecan involve a large amount of data and require a large number ofcomputational operations, which can cause delays in processing thequeries. Advantageously, the data intake and query system also employs anumber of unique acceleration techniques that have been developed tospeed up analysis operations performed at search time. These techniquesinclude: (1) performing search operations in parallel across multipleindexers; (2) using a keyword index; (3) using a high performanceanalytics store; and (4) accelerating the process of generating reports.These novel techniques are described in more detail below.

2.13.1 Aggregation Technique

To facilitate faster query processing, a query can be structured suchthat multiple indexers perform the query in parallel, while aggregationof search results from the multiple indexers is performed locally at thesearch head. For example, FIG. 16 is an example search query receivedfrom a client and executed by search peers, in accordance with exampleimplementations. FIG. 16 illustrates how a search query 1602 receivedfrom a client at a search head 210 can split into two phases, including:(1) subtasks 1604 (e.g., data retrieval or simple filtering) that may beperformed in parallel by indexers 206 for execution, and (2) a searchresults aggregation operation 1606 to be executed by the search headwhen the results are ultimately collected from the indexers.

During operation, upon receiving search query 1602, a search head 210determines that a portion of the operations involved with the searchquery may be performed locally by the search head. The search headmodifies search query 1602 by substituting “stats” (create aggregatestatistics over results sets received from the indexers at the searchhead) with “prestats” (create statistics by the indexer from localresults set) to produce search query 1604, and then distributes searchquery 1604 to distributed indexers, which are also referred to as“search peers” or “peer indexers.” Note that search queries maygenerally specify search criteria or operations to be performed onevents that meet the search criteria. Search queries may also specifyfield names, as well as search criteria for the values in the fields oroperations to be performed on the values in the fields. Moreover, thesearch head may distribute the full search query to the search peers asillustrated in FIG. 6A, or may alternatively distribute a modifiedversion (e.g., a more restricted version) of the search query to thesearch peers. In this example, the indexers are responsible forproducing the results and sending them to the search head. After theindexers return the results to the search head, the search headaggregates the received results 1606 to form a single search result set.By executing the query in this manner, the system effectivelydistributes the computational operations across the indexers whileminimizing data transfers.

2.13.2 Keyword Index

As described above with reference to the flow charts in FIG. 5A and FIG.6A, data intake and query system 108 can construct and maintain one ormore keyword indices to quickly identify events containing specifickeywords. This technique can greatly speed up the processing of queriesinvolving specific keywords. As mentioned above, to build a keywordindex, an indexer first identifies a set of keywords. Then, the indexerincludes the identified keywords in an index, which associates eachstored keyword with references to events containing that keyword, or tolocations within events where that keyword is located. When an indexersubsequently receives a keyword-based query, the indexer can access thekeyword index to quickly identify events containing the keyword.

2.13.3 High Performance Analytics Store

To speed up certain types of queries, some implementations of system 108create a high performance analytics store, which is referred to as a“summarization table,” that contains entries for specific field-valuepairs. Each of these entries keeps track of instances of a specificvalue in a specific field in the events and includes references toevents containing the specific value in the specific field. For example,an example entry in a summarization table can keep track of occurrencesof the value “94107” in a “ZIP code” field of a set of events and theentry includes references to all of the events that contain the value“94107” in the ZIP code field. This optimization technique enables thesystem to quickly process queries that seek to determine how many eventshave a particular value for a particular field. To this end, the systemcan examine the entry in the summarization table to count instances ofthe specific value in the field without having to go through theindividual events or perform data extractions at search time. Also, ifthe system needs to process all events that have a specific field-valuecombination, the system can use the references in the summarizationtable entry to directly access the events to extract further informationwithout having to search all of the events to find the specificfield-value combination at search time.

In some implementations, the system maintains a separate summarizationtable for each of the above-described time-specific buckets that storesevents for a specific time range. A bucket-specific summarization tableincludes entries for specific field-value combinations that occur inevents in the specific bucket. Alternatively, the system can maintain aseparate summarization table for each indexer. The indexer-specificsummarization table includes entries for the events in a data store thatare managed by the specific indexer. Indexer-specific summarizationtables may also be bucket-specific.

The summarization table can be populated by running a periodic querythat scans a set of events to find instances of a specific field-valuecombination, or alternatively instances of all field-value combinationsfor a specific field. A periodic query can be initiated by a user, orcan be scheduled to occur automatically at specific time intervals. Aperiodic query can also be automatically launched in response to a querythat asks for a specific field-value combination.

In some cases, when the summarization tables may not cover all of theevents that are relevant to a query, the system can use thesummarization tables to obtain partial results for the events that arecovered by summarization tables, but may also have to search throughother events that are not covered by the summarization tables to produceadditional results. These additional results can then be combined withthe partial results to produce a final set of results for the query. Thesummarization table and associated techniques are described in moredetail in U.S. Pat. No. 8,682,925, entitled “Distributed HighPerformance Analytics Store”, issued on 25 Mar. 2014, U.S. Pat. No.9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCE ANALYTICS STOREWITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO AN EVENT QUERY”,issued on 8 Sep. 2015, and U.S. patent application Ser. No. 14/815,973,entitled “GENERATING AND STORING SUMMARIZATION TABLES FOR SETS OFSEARCHABLE EVENTS”, filed on 1 Aug. 2015, each of which is herebyincorporated by reference in its entirety for all purposes.

To speed up certain types of queries, e.g., frequently encounteredqueries or computationally intensive queries, some implementations ofsystem 108 create a high performance analytics store, which is referredto as a “summarization table,” (also referred to as a “lexicon” or“inverted index”) that contains entries for specific field-value pairs.Each of these entries keeps track of instances of a specific value in aspecific field in the event data and includes references to eventscontaining the specific value in the specific field. For example, anexample entry in an inverted index can keep track of occurrences of thevalue “94107” in a “ZIP code” field of a set of events and the entryincludes references to all of the events that contain the value “94107”in the ZIP code field. Creating the inverted index data structure avoidsneeding to incur the computational overhead each time a statisticalquery needs to be run on a frequently encountered field-value pair. Inorder to expedite queries, in most implementations, the search enginewill employ the inverted index separate from the raw record data storeto generate responses to the received queries.

Note that the term “summarization table” or “inverted index” as usedherein is a data structure that may be generated by an indexer thatincludes at least field names and field values that have been extractedand/or indexed from event records. An inverted index may also includereference values that point to the location(s) in the field searchabledata store where the event records that include the field may be found.Also, an inverted index may be stored using well-known compressiontechniques to reduce its storage size.

Further, note that the term “reference value” (also referred to as a“posting value”) as used herein is a value that references the locationof a source record in the field searchable data store. In someimplementations, the reference value may include additional informationabout each record, such as timestamps, record size, meta-data, or thelike. Each reference value may be a unique identifier which may be usedto access the event data directly in the field searchable data store. Insome implementations, the reference values may be ordered based on eachevent record's timestamp. For example, if numbers are used asidentifiers, they may be sorted so event records having a latertimestamp always have a lower valued identifier than event records withan earlier timestamp, or vice-versa. Reference values are often includedin inverted indexes for retrieving and/or identifying event records.

In one or more implementations, an inverted index is generated inresponse to a user-initiated collection query. The term “collectionquery” as used herein refers to queries that include commands thatgenerate summarization information and inverted indexes (orsummarization tables) from event records stored in the field searchabledata store.

Note that a collection query is a special type of query that can beuser-generated and is used to create an inverted index. A collectionquery is not the same as a query that is used to call up or invoke apre-existing inverted index. In one or more implementation, a query cancomprise an initial step that calls up a pre-generated inverted index onwhich further filtering and processing can be performed. For example,referring back to FIG. 13 , a set of events generated at block 1320 byeither using a “collection” query to create a new inverted index or bycalling up a pre-generated inverted index. A query with severalpipelined steps will start with a pre-generated index to accelerate thequery.

FIG. 7C illustrates the manner in which an inverted index is created andused in accordance with the disclosed implementations. As shown in FIG.7C, an inverted index 722 can be created in response to a user-initiatedcollection query using the event data 723 stored in the raw record datastore. For example, a non-limiting example of a collection query mayinclude “collect clientip=127.0.0.1” which may result in an invertedindex 722 being generated from the event data 723 as shown in FIG. 7C.Each entry in inverted index 722 includes an event reference value thatreferences the location of a source record in the field searchable datastore. The reference value may be used to access the original eventrecord directly from the field searchable data store.

In one or more implementations, if one or more of the queries is acollection query, the responsive indexers may generate summarizationinformation based on the fields of the event records located in thefield searchable data store. In at least one of the variousimplementations, one or more of the fields used in the summarizationinformation may be listed in the collection query and/or they may bedetermined based on terms included in the collection query. For example,a collection query may include an explicit list of fields to summarize.Or, in at least one of the various implementations, a collection querymay include terms or expressions that explicitly define the fields,e.g., using regex rules. In FIG. 7C, prior to running the collectionquery that generates the inverted index 722, the field name “clientip”may need to be defined in a configuration file by specifying the“access_combined” source type and a regular expression rule to parse outthe client IP address. Alternatively, the collection query may containan explicit definition for the field name “clientip” which may obviatethe need to reference the configuration file at search time.

In one or more implementations, collection queries may be saved andscheduled to run periodically. These scheduled collection queries mayperiodically update the summarization information corresponding to thequery. For example, if the collection query that generates invertedindex 722 is scheduled to run periodically, one or more indexers wouldperiodically search through the relevant buckets to update invertedindex 722 with event data for any new events with the “clientip” valueof “127.0.0.1.”

In some implementations, the inverted indexes that include fields,values, and reference value (e.g., inverted index 722) for event recordsmay be included in the summarization information provided to the user.In other implementations, a user may not be interested in specificfields and values contained in the inverted index, but may need toperform a statistical query on the data in the inverted index. Forexample, referencing the example of FIG. 7C rather than viewing thefields within summarization table 722, a user may want to generate acount of all client requests from IP address “127.0.0.1.” In this case,the search engine would simply return a result of “4” rather thanincluding details about the inverted index 722 in the informationprovided to the user.

The pipelined search language, e.g., SPL of the SPLUNK® ENTERPRISEsystem can be used to pipe the contents of an inverted index to astatistical query using the “stats” command for example. A “stats” queryrefers to queries that generate result sets that may produce aggregateand statistical results from event records, e.g., average, mean, max,min, rms, etc. Where sufficient information is available in an invertedindex, a “stats” query may generate their result sets rapidly from thesummarization information available in the inverted index rather thandirectly scanning event records. For example, the contents of invertedindex 722 can be pipelined to a stats query, e.g., a “count” functionthat counts the number of entries in the inverted index and returns avalue of “4.” In this way, inverted indexes may enable various statsqueries to be performed absent scanning or search the event records.Accordingly, this optimization technique enables the system to quicklyprocess queries that seek to determine how many events have a particularvalue for a particular field. To this end, the system can examine theentry in the inverted index to count instances of the specific value inthe field without having to go through the individual events or performdata extractions at search time.

In some implementations, the system maintains a separate inverted indexfor each of the above-described time-specific buckets that stores eventsfor a specific time range. A bucket-specific inverted index includesentries for specific field-value combinations that occur in events inthe specific bucket. Alternatively, the system can maintain a separateinverted index for each indexer. The indexer-specific inverted indexincludes entries for the events in a data store that are managed by thespecific indexer. Indexer-specific inverted indexes may also bebucket-specific. In at least one or more implementations, if one or moreof the queries is a stats query, each indexer may generate a partialresult set from previously generated summarization information. Thepartial result sets may be returned to the search head that received thequery and combined into a single result set for the query

As mentioned above, the inverted index can be populated by running aperiodic query that scans a set of events to find instances of aspecific field-value combination, or alternatively instances of allfield-value combinations for a specific field. A periodic query can beinitiated by a user, or can be scheduled to occur automatically atspecific time intervals. A periodic query can also be automaticallylaunched in response to a query that asks for a specific field-valuecombination. In some implementations, if summarization information isabsent from an indexer that includes responsive event records, furtheractions may be taken, such as, the summarization information maygenerated on the fly, warnings may be provided the user, the collectionquery operation may be halted, the absence of summarization informationmay be ignored, or the like, or combination thereof.

In one or more implementations, an inverted index may be set up toupdate continually. For example, the query may ask for the invertedindex to update its result periodically, e.g., every hour. In suchinstances, the inverted index may be a dynamic data structure that isregularly updated to include information regarding incoming events.

In some cases, e.g., where a query is executed before an inverted indexupdates, when the inverted index may not cover all of the events thatare relevant to a query, the system can use the inverted index to obtainpartial results for the events that are covered by inverted index, butmay also have to search through other events that are not covered by theinverted index to produce additional results on the fly. In other words,an indexer would need to search through event data on the data store tosupplement the partial results. These additional results can then becombined with the partial results to produce a final set of results forthe query. Note that in typical instances where an inverted index is notcompletely up to date, the number of events that an indexer would needto search through to supplement the results from the inverted indexwould be relatively small. In other words, the search to get the mostrecent results can be quick and efficient because only a small number ofevent records will be searched through to supplement the informationfrom the inverted index. The inverted index and associated techniquesare described in more detail in U.S. Pat. No. 8,682,925, entitled“Distributed High Performance Analytics Store”, issued on 25 Mar. 2014,U.S. Pat. No. 9,128,985, entitled “SUPPLEMENTING A HIGH PERFORMANCEANALYTICS STORE WITH EVALUATION OF INDIVIDUAL EVENTS TO RESPOND TO ANEVENT QUERY”, filed on 31 Jan. 2014, and U.S. patent application Ser.No. 14/815,973, entitled “STORAGE MEDIUM AND CONTROL DEVICE”, filed on21 Feb. 2014, each of which is hereby incorporated by reference in itsentirety.

2.13.3.1 Extracting Event Data Using Posting

In one or more implementations, if the system needs to process allevents that have a specific field-value combination, the system can usethe references in the inverted index entry to directly access the eventsto extract further information without having to search all of theevents to find the specific field-value combination at search time. Inother words, the system can use the reference values to locate theassociated event data in the field searchable data store and extractfurther information from those events, e.g., extract further fieldvalues from the events for purposes of filtering or processing or both.

The information extracted from the event data using the reference valuescan be directed for further filtering or processing in a query using thepipeline search language. The pipelined search language will, in oneimplementation, include syntax that can direct the initial filteringstep in a query to an inverted index. In one implementation, a userwould include syntax in the query that explicitly directs the initialsearching or filtering step to the inverted index.

Referencing the example in FIG. 15 , if the user determines that sheneeds the user id fields associated with the client requests from IPaddress “127.0.0.1,” instead of incurring the computational overhead ofperforming a brand new search or re-generating the inverted index withan additional field, the user can generate a query that explicitlydirects or pipes the contents of the already generated inverted index1502 to another filtering step requesting the user ids for the entriesin inverted index 1502 where the server response time is greater than“0.0900” microseconds. The search engine would use the reference valuesstored in inverted index 722 to retrieve the event data from the fieldsearchable data store, filter the results based on the “response time”field values and, further, extract the user id field from the resultingevent data to return to the user. In the present instance, the user ids“frank” and “carlos” would be returned to the user from the generatedresults table 722.

In one implementation, the same methodology can be used to pipe thecontents of the inverted index to a processing step. In other words, theuser is able to use the inverted index to efficiently and quicklyperform aggregate functions on field values that were not part of theinitially generated inverted index. For example, a user may want todetermine an average object size (size of the requested gif) requestedby clients from IP address “127.0.0.1.” In this case, the search enginewould again use the reference values stored in inverted index 722 toretrieve the event data from the field searchable data store and,further, extract the object size field values from the associated events731, 732, 733 and 734. Once, the corresponding object sizes have beenextracted (i.e. 2326, 2900, 2920, and 5000), the average can be computedand returned to the user.

In one implementation, instead of explicitly invoking the inverted indexin a user-generated query, e.g., by the use of special commands orsyntax, the SPLUNK® ENTERPRISE system can be configured to automaticallydetermine if any prior-generated inverted index can be used to expeditea user query. For example, the user's query may request the averageobject size (size of the requested gif) requested by clients from IPaddress “127.0.0.1.” without any reference to or use of inverted index722. The search engine, in this case, would automatically determine thatan inverted index 722 already exists in the system that may expeditethis query. In one implementation, prior to running any searchcomprising a field-value pair, for example, a search engine may searchthough all the existing inverted indexes to determine if a pre-generatedinverted index may be used to expedite the search comprising thefield-value pair. Accordingly, the search engine would automatically usethe pre-generated inverted index, e.g., index 722 to generate theresults without any user-involvement that directs the use of the index.

Using the reference values in an inverted index to be able to directlyaccess the event data in the field searchable data store and extractfurther information from the associated event data for further filteringand processing is highly advantageous because it avoids incurring thecomputation overhead of regenerating the inverted index with additionalfields or performing a new search.

The data intake and query system includes one or more forwarders thatreceive raw machine data from a variety of input data sources, and oneor more indexers that process and store the data in one or more datastores. By distributing events among the indexers and data stores, theindexers can analyze events for a query in parallel. In one or moreimplementations, a multiple indexer implementation of the search systemwould maintain a separate and respective inverted index for each of theabove-described time-specific buckets that stores events for a specifictime range. A bucket-specific inverted index includes entries forspecific field-value combinations that occur in events in the specificbucket. As explained above, a search head would be able to correlate andsynthesize data from across the various buckets and indexers.

This feature advantageously expedites searches because instead ofperforming a computationally intensive search in a centrally locatedinverted index that catalogues all the relevant events, an indexer isable to directly search an inverted index stored in a bucket associatedwith the time-range specified in the query. This allows the search to beperformed in parallel across the various indexers. Further, if the queryrequests further filtering or processing to be conducted on the eventdata referenced by the locally stored bucket-specific inverted index,the indexer is able to simply access the event records stored in theassociated bucket for further filtering and processing instead ofneeding to access a central repository of event records, which woulddramatically add to the computational overhead.

In one implementation, there may be multiple buckets associated with thetime-range specified in a query. If the query is directed to an invertedindex, or if the search engine automatically determines that using aninverted index would expedite the processing of the query, the indexerswill search through each of the inverted indexes associated with thebuckets for the specified time-range. This feature allows the HighPerformance Analytics Store to be scaled easily.

In certain instances, where a query is executed before a bucket-specificinverted index updates, when the bucket-specific inverted index may notcover all of the events that are relevant to a query, the system can usethe bucket-specific inverted index to obtain partial results for theevents that are covered by bucket-specific inverted index, but may alsohave to search through the event data in the bucket associated with thebucket-specific inverted index to produce additional results on the fly.In other words, an indexer would need to search through event datastored in the bucket (that was not yet processed by the indexer for thecorresponding inverted index) to supplement the partial results from thebucket-specific inverted index.

FIG. 7D presents a flowchart illustrating how an inverted index in apipelined search query can be used to determine a set of event data thatcan be further limited by filtering or processing in accordance with thedisclosed implementations.

At block 742, a query is received by a data intake and query system. Insome implementations, the query can be received as a user generatedquery entered into a search bar of a graphical user search interface.The search interface also includes a time range control element thatenables specification of a time range for the query.

At block 744, an inverted index is retrieved. Note, that the invertedindex can be retrieved in response to an explicit user search commandinputted as part of the user generated query. Alternatively, the searchengine can be configured to automatically use an inverted index if itdetermines that using the inverted index would expedite the servicing ofthe user generated query. Each of the entries in an inverted index keepstrack of instances of a specific value in a specific field in the eventdata and includes references to events containing the specific value inthe specific field. In order to expedite queries, in mostimplementations, the search engine will employ the inverted indexseparate from the raw record data store to generate responses to thereceived queries.

At block 746, the query engine determines if the query contains furtherfiltering and processing steps. If the query contains no furthercommands, then, in one implementation, summarization information can beprovided to the user at block 754.

If, however, the query does contain further filtering and processingcommands, then at block 750, the query engine determines if the commandsrelate to further filtering or processing of the data extracted as partof the inverted index or whether the commands are directed to using theinverted index as an initial filtering step to further filter andprocess event data referenced by the entries in the inverted index. Ifthe query can be completed using data already in the generated invertedindex, then the further filtering or processing steps, e.g., a “count”number of records function, “average” number of records per hour etc.are performed and the results are provided to the user at block 752.

If, however, the query references fields that are not extracted in theinverted index, then the indexers will access event data pointed to bythe reference values in the inverted index to retrieve any furtherinformation required at block 756. Subsequently, any further filteringor processing steps are performed on the fields extracted directly fromthe event data and the results are provided to the user at step 758.

2.13.3.2 Accelerating Report Generation

In some implementations, a data server system such as the data intakeand query system can accelerate the process of periodically generatingupdated reports based on query results. To accelerate this process, asummarization engine automatically examines the query to determinewhether generation of updated reports can be accelerated by creatingintermediate summaries. If reports can be accelerated, the summarizationengine periodically generates a summary covering data obtained during alatest non-overlapping time period. For example, where the query seeksevents meeting a specified criteria, a summary for the time periodincludes only events within the time period that meet the specifiedcriteria. Similarly, if the query seeks statistics calculated from theevents, such as the number of events that match the specified criteria,then the summary for the time period includes the number of events inthe period that match the specified criteria.

In addition to the creation of the summaries, the summarization engineschedules the periodic updating of the report associated with the query.During each scheduled report update, the query engine determines whetherintermediate summaries have been generated covering portions of the timeperiod covered by the report update. If so, then the report is generatedbased on the information contained in the summaries. Also, if additionalevent data has been received and has not yet been summarized, and isrequired to generate the complete report, the query can be run on theseadditional events. Then, the results returned by this query on theadditional events, along with the partial results obtained from theintermediate summaries, can be combined to generate the updated report.This process is repeated each time the report is updated. Alternatively,if the system stores events in buckets covering specific time ranges,then the summaries can be generated on a bucket-by-bucket basis. Notethat producing intermediate summaries can save the work involved inre-running the query for previous time periods, so advantageously onlythe newer events needs to be processed while generating an updatedreport. These report acceleration techniques are described in moredetail in U.S. Pat. No. 8,589,403, entitled “Compressed Journaling InEvent Tracking Files For Metadata Recovery And Replication”, issued on19 Nov. 2013, U.S. Pat. No. 8,412,696, entitled “Real Time Searching AndReporting”, issued on 2 Apr. 2011, and U.S. Pat. Nos. 8,589,375 and8,589,432, both also entitled “REAL TIME SEARCHING AND REPORTING”, bothissued on 19 Nov. 2013, each of which is hereby incorporated byreference in its entirety for all purposes.

2.14 Security Features

The data intake and query system provides various schemas, dashboards,and visualizations that simplify developers' tasks to createapplications with additional capabilities. One such application is thean enterprise security application, such as SPLUNK® ENTERPRISE SECURITY,which performs monitoring and alerting operations and includes analyticsto facilitate identifying both known and unknown security threats basedon large volumes of data stored by the data intake and query system. Theenterprise security application provides the security practitioner withvisibility into security-relevant threats found in the enterpriseinfrastructure by capturing, monitoring, and reporting on data fromenterprise security devices, systems, and applications. Through the useof the data intake and query system searching and reportingcapabilities, the enterprise security application provides a top-downand bottom-up view of an organization's security posture.

The enterprise security application leverages the data intake and querysystem search-time normalization techniques, saved searches, andcorrelation searches to provide visibility into security-relevantthreats and activity and generate notable events for tracking. Theenterprise security application enables the security practitioner toinvestigate and explore the data to find new or unknown threats that donot follow signature-based patterns.

Conventional Security Information and Event Management (STEM) systemslack the infrastructure to effectively store and analyze large volumesof security-related data. Traditional SIEM systems typically use fixedschemas to extract data from pre-defined security-related fields at dataingestion time and store the extracted data in a relational database.This traditional data extraction process (and associated reduction indata size) that occurs at data ingestion time inevitably hampers futureincident investigations that may need original data to determine theroot cause of a security issue, or to detect the onset of an impendingsecurity threat.

In contrast, the enterprise security application system stores largevolumes of minimally-processed security-related data at ingestion timefor later retrieval and analysis at search time when a live securitythreat is being investigated. To facilitate this data retrieval process,the enterprise security application provides pre-specified schemas forextracting relevant values from the different types of security-relatedevents and enables a user to define such schemas.

The enterprise security application can process many types ofsecurity-related information. In general, this security-relatedinformation can include any information that can be used to identifysecurity threats. For example, the security-related information caninclude network-related information, such as IP addresses, domain names,asset identifiers, network traffic volume, uniform resource locatorstrings, and source addresses. The process of detecting security threatsfor network-related information is further described in U.S. Pat. No.8,826,434, entitled “SECURITY THREAT DETECTION BASED ON INDICATIONS INBIG DATA OF ACCESS TO NEWLY REGISTERED DOMAINS”, issued on 2 Sep. 2014,U.S. Pat. No. 9,215,240, entitled “INVESTIGATIVE AND DYNAMIC DETECTIONOF POTENTIAL SECURITY-THREAT INDICATORS FROM EVENTS IN BIG DATA”, issuedon 15 Dec. 2015, U.S. Pat. No. 9,173,801, entitled “GRAPHIC DISPLAY OFSECURITY THREATS BASED ON INDICATIONS OF ACCESS TO NEWLY REGISTEREDDOMAINS”, issued on 3 Nov. 2015, U.S. Pat. No. 9,248,068, entitled“SECURITY THREAT DETECTION OF NEWLY REGISTERED DOMAINS”, issued on 2Feb. 2016, U.S. Pat. No. 9,426,172, entitled “SECURITY THREAT DETECTIONUSING DOMAIN NAME ACCESSES”, issued on 23 Aug. 2016, and U.S. Pat. No.9,432,396, entitled “SECURITY THREAT DETECTION USING DOMAIN NAMEREGISTRATIONS”, issued on 30 Aug. 2016, each of which is herebyincorporated by reference in its entirety for all purposes.Security-related information can also include malware infection data andsystem configuration information, as well as access control information,such as login/logout information and access failure notifications. Thesecurity-related information can originate from various sources within adata center, such as hosts, virtual machines, storage devices andsensors. The security-related information can also originate fromvarious sources in a network, such as routers, switches, email servers,proxy servers, gateways, firewalls and intrusion-detection systems.

During operation, the enterprise security application facilitatesdetecting “notable events” that are likely to indicate a securitythreat. A notable event represents one or more anomalous incidents, theoccurrence of which can be identified based on one or more events (e.g.,time stamped portions of raw machine data) fulfilling pre-specifiedand/or dynamically-determined (e.g., based on machine-learning) criteriadefined for that notable event. Examples of notable events include therepeated occurrence of an abnormal spike in network usage over a periodof time, a single occurrence of unauthorized access to system, a hostcommunicating with a server on a known threat list, and the like. Thesenotable events can be detected in a number of ways, such as: (1) a usercan notice a correlation in events and can manually identify that acorresponding group of one or more events amounts to a notable event; or(2) a user can define a “correlation search” specifying criteria for anotable event, and every time one or more events satisfy the criteria,the application can indicate that the one or more events correspond to anotable event; and the like. A user can alternatively select apre-defined correlation search provided by the application. Note thatcorrelation searches can be run continuously or at regular intervals(e.g., every hour) to search for notable events. Upon detection, notableevents can be stored in a dedicated “notable events index,” which can besubsequently accessed to generate various visualizations containingsecurity-related information. Also, alerts can be generated to notifysystem operators when important notable events are discovered.

The enterprise security application provides various visualizations toaid in discovering security threats, such as a “key indicators view”that enables a user to view security metrics, such as counts ofdifferent types of notable events. For example, FIG. 17A illustrates anexample key indicators view 1700 that comprises a dashboard, which candisplay a value 1701, for various security-related metrics, such asmalware infections 1702. It can also display a change in a metric value1703, which indicates that the number of malware infections increased by63 during the preceding interval. Key indicators view 1700 additionallydisplays a histogram panel 1704 that displays a histogram of notableevents organized by urgency values, and a histogram of notable eventsorganized by time intervals. This key indicators view is described infurther detail in pending U.S. patent application Ser. No. 13/956,338,entitled “Key Indicators View”, filed on 31 Jul. 2013, and which ishereby incorporated by reference in its entirety for all purposes.

These visualizations can also include an “incident review dashboard”that enables a user to view and act on “notable events.” These notableevents can include: (1) a single event of high importance, such as anyactivity from a known web attacker; or (2) multiple events thatcollectively warrant review, such as a large number of authenticationfailures on a host followed by a successful authentication. For example,FIG. 17B illustrates an example incident review dashboard 1710 thatincludes a set of incident attribute fields 1711 that, for example,enables a user to specify a time range field 1712 for the displayedevents. It also includes a timeline 1713 that graphically illustratesthe number of incidents that occurred in time intervals over theselected time range. It additionally displays an events list 1714 thatenables a user to view a list of all of the notable events that matchthe criteria in the incident attributes fields 1711. To facilitateidentifying patterns among the notable events, each notable event can beassociated with an urgency value (e.g., low, medium, high, critical),which is indicated in the incident review dashboard. The urgency valuefor a detected event can be determined based on the severity of theevent and the priority of the system component associated with theevent.

2.15 Data Center Monitoring

As mentioned above, the data intake and query platform provides variousfeatures that simplify the developers' task to create variousapplications. One such application is a virtual machine monitoringapplication, such as SPLUNK® APP FOR VMWARE® that provides operationalvisibility into granular performance metrics, logs, tasks and events,and topology from hosts, virtual machines and virtual centers. Itempowers administrators with an accurate real-time picture of the healthof the environment, proactively identifying performance and capacitybottlenecks.

Conventional data-center-monitoring systems lack the infrastructure toeffectively store and analyze large volumes of machine-generated data,such as performance information and log data obtained from the datacenter. In conventional data-center-monitoring systems,machine-generated data is typically pre-processed prior to being stored,for example, by extracting pre-specified data items and storing them ina database to facilitate subsequent retrieval and analysis at searchtime. However, the rest of the data is not saved and discarded duringpre-processing.

In contrast, the virtual machine monitoring application stores largevolumes of minimally processed machine data, such as performanceinformation and log data, at ingestion time for later retrieval andanalysis at search time when a live performance issue is beinginvestigated. In addition to data obtained from various log files, thisperformance-related information can include values for performancemetrics obtained through an application programming interface (API)provided as part of the vSphere Hypervisor™ system distributed byVMware, Inc. of Palo Alto, Calif. For example, these performance metricscan include: (1) CPU-related performance metrics; (2) disk-relatedperformance metrics; (3) memory-related performance metrics; (4)network-related performance metrics; (5) energy-usage statistics; (6)data-traffic-related performance metrics; (7) overall systemavailability performance metrics; (8) cluster-related performancemetrics; and (9) virtual machine performance statistics. Suchperformance metrics are described in U.S. patent application Ser. No.14/167,316, entitled “Correlation For User-Selected Time Ranges OfValues For Performance Metrics Of Components In AnInformation-Technology Environment With Log Data From ThatInformation-Technology Environment”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

To facilitate retrieving information of interest from performance dataand log files, the virtual machine monitoring application providespre-specified schemas for extracting relevant values from differenttypes of performance-related events, and also enables a user to definesuch schemas.

The virtual machine monitoring application additionally provides variousvisualizations to facilitate detecting and diagnosing the root cause ofperformance problems. For example, one such visualization is a“proactive monitoring tree” that enables a user to easily view andunderstand relationships among various factors that affect theperformance of a hierarchically structured computing system. Thisproactive monitoring tree enables a user to easily navigate thehierarchy by selectively expanding nodes representing various entities(e.g., virtual centers or computing clusters) to view performanceinformation for lower-level nodes associated with lower-level entities(e.g., virtual machines or host systems). Example node-expansionoperations are illustrated in FIG. 17C, wherein nodes 1733 and 1734 areselectively expanded. Note that nodes 1731-1739 can be displayed usingdifferent patterns or colors to represent different performance states,such as a critical state, a warning state, a normal state or anunknown/offline state. The ease of navigation provided by selectiveexpansion in combination with the associated performance-stateinformation enables a user to quickly diagnose the root cause of aperformance problem. The proactive monitoring tree is described infurther detail in U.S. Pat. No. 9,185,007, entitled “PROACTIVEMONITORING TREE WITH SEVERITY STATE SORTING”, issued on 10 Nov. 2015,and U.S. Pat. No. 9,426,045, also entitled “PROACTIVE MONITORING TREEWITH SEVERITY STATE SORTING”, issued on 23 Aug. 2016, each of which ishereby incorporated by reference in its entirety for all purposes.

The virtual machine monitoring application also provides a userinterface that enables a user to select a specific time range and thenview heterogeneous data comprising events, log data, and associatedperformance metrics for the selected time range. For example, the screenillustrated in FIG. 17D displays a listing of recent “tasks and events”and a listing of recent “log entries” for a selected time range above aperformance-metric graph for “average CPU core utilization” for theselected time range. Note that a user is able to operate pull-down menus1742 to selectively display different performance metric graphs for theselected time range. This enables the user to correlate trends in theperformance-metric graph with corresponding event and log data toquickly determine the root cause of a performance problem. This userinterface is described in more detail in U.S. patent application Ser.No. 14/167,316, entitled “Correlation For User-Selected Time Ranges OfValues For Performance Metrics Of Components In AnInformation-Technology Environment With Log Data From ThatInformation-Technology Environment”, filed on 29 Jan. 2014, and which ishereby incorporated by reference in its entirety for all purposes.

3. Sharing Physical Data for Remote Collaboration Sessions

At least one problem with conventional approaches for multi-usercollaboration of real-world environments is that devices remote to agiven location have difficulty providing a complete, useful, orcontrollable view of the environment. For example, a host user may use ahost device to share image data or video data captured by a camera at agiven location. However, upon receiving the shared image data or videodata, the remote user is limited to the view that is presented by thehost, which in turn limits the ability of the remote user to view andinteract with the physical object using the remote device. As a result,the remote user may be forced to provide instructions to a host user toreposition the camera. Further, the remote device provides limitedcontrols for interacting with the portions of the real-worldenvironment. Consequently, the remote user must rely on the host user tointeract with physical objects on behalf of the remote user. Further,the remote user may provide standardized guidance that is overly vague,limiting the actions that the host user performs on portions of theenvironment. In instances where the host user cannot sufficiently guidethe remote user to perform actions, the remote user must travel to thereal-world environment.

Accordingly, in various implementations disclosed herein, a networkedcomputing environment includes a host device and one or more remotedevices that interact in a common remote collaboration session. Inparticular, a host device uses various sensors to scan a scene that is aportion of a real-world environment. For example, the host device mayscan the environment using a depth sensor to acquire three-dimensional(3D) depth data, and an imaging sensor to acquire two-dimensional (2D)surface data. The host device combines correlated 2D surface data and 3Ddepth data into an extended reality (XR) stream and transmits the XRstream to a remote device.

The remote device receives the XR stream and renders, based on thecorrelated 2D surface data and 3D depth data encapsulated in the XRstream, a portion of the real-world environment for presentation at thelocation of the remote device. In various embodiments, the remoteenvironment includes a digital representation of a real-world asset thatis included in the real-world environment. In various embodiments, theremote device retrieves data associated with the real-world asset andpresents the data within the remote environment. Further, the remotedevice enables the user to navigate and interact with portions of thereproduced scene independent of the portion of the scene that the hostdevice is currently viewing. These techniques are described below infurther detail in conjunction with FIGS. 18A-30 .

3.1 Networked Remote Collaboration System

FIG. 18A illustrates a network architecture that enables securecommunications between an extended reality application, a mobileoperations application, and an on-premises environment for the dataintake and query system, in accordance with example implementations.

In various embodiments, cloud-based data intake and query system 306,executing in cloud environment 1805, may serve as a secure bridgebetween extended reality (XR) application 1814 and an on-premisesenvironment 1860. In other implementations, the on-premises environment1860 may be omitted and the entire computational process may be carriedout in one or more aspects or components of cloud environment 1805. Invarious embodiments, cloud environment 1805 may include cloud-based dataintake and query system 306, which communicates with data intake andquery system 108 via network 304. Cloud environment 1805 may furtherinclude middleware code 1852 and/or push notification service 1854,which communicate with extended reality application 1814 via network420. In various embodiments, network 304 and network 420 may be the samenetwork or may include one or more shared network components thatcommunicate with both network 304 and network 420.

In various embodiments, an asset (e.g., physical object, machine,particular area, etc.) may have a tag that encodes or otherwise includesdata. The data in the tag includes a textual and/or numerical stringthat operates as a unique identifier (UID). The tag is provided by anentity that owns or operates the environment in which the asset resides.Additionally or alternatively, the entity may assign the UID to theasset without encoding the UID in a tag. Host device 1804 scans theasset and/or tags associated with the asset, determines the uniqueidentifier for the asset and uses the unique identifier to receive fieldvalues, extracted from events, which are associated with the asset. Invarious embodiments, host device 1804, remote device 1810 (e.g., 1810-1,1810-2, etc.) and/or data intake and query system 108 may generatecontent (e.g., schemas, dashboards, cards, and/or visualizations) basedon the extracted field values.

Extended reality application 1814 and/or mobile operations application1816 may display the content to the user via host device 1804 and/orremote device 1819. For example, XR application 1814 may generate anextended reality workspace that encapsulates the asset and presents datavalues that are associated with the unique identifier. Portions of theXR workspace may include panels that display the content. For example,one or more display panels may include various schemas, dashboards,cards, and/or visualizations generated from the extracted field values.In some embodiments, XR application 1814 may cause the content to bedisplayed in a continuous manner, as host device 1804 and/or remotedevice 1810 view different portions of the extended reality environment.Additionally or alternatively, the XR workspace may also includedirectional indicators, such as pointers at the edge of the displaydevice, indicating the position of an asset and/or portions of the XRworkspace relative to the position and/or orientation of the camera.

In this manner, a user may move through an environment and visuallydetermine the status of various entities, such as machines, people,and/or assets, in that environment. For example, the user may be able toscan a particular asset or tag of the particular asset in order toobtain information related to the asset. Upon receiving variousvisualizations based on the obtained information, the user may thenanalyze the information to determine whether the asset needs attention,repair, or replacement.

In various embodiments, mobile operations application 1816 executing onone or more remote devices 1810 (e.g., remote device 1810-2) may presenta non-XR environment that corresponds to an XR environment presented byXR application 1814. In such instances, a user may navigate the non-XRenvironment without physically moving remote device 1810-2. Instead, theuser may select various navigation controls to change the position ofmobile device 1810-2 within the non-XR environment, changing the virtualperspective of mobile device 1810-2 in relation to one or more assetsincluded in the non-XR environment.

In some embodiments, XR application 1814 and/or mobile operationsapplication 1816 executing on host device 1804 and/or remote device 1810may establish secure, bidirectional communications with data intake andquery system 108. For example, in some embodiments, a persistent,always-open, asynchronous socket for bidirectional communications (e.g.,a Web Socket connection) through a firewall of on-premises environment1860 may be established between data intake and query system 108 andcloud-based data intake and query system 306. Cloud-based data intakeand query system 306 may then communicate with XR application 1814and/or mobile operations application 1816 via middleware code 1852executing in cloud environment 1805.

Additionally or alternatively, in some embodiments, cloud-based dataintake and query system 306 and/or middleware code 1852 may communicatewith XR application 1814 and/or mobile operations application 1816 via apush notification service 1854, such as Apple Push Notification service(APNs) or Google Cloud Messaging (GCM). For example, data intake andquery system 108 may, based on the unique identifier, output to one ormore devices 1804, 1810 content that includes real-time data associatedwith a particular asset. The content may then be presented by one ormore devices 1804, 1810.

For example, mobile operations application 1816 may present the contentin a window provided by remote device 1810-2. In some embodiments, XRapplication 1814 may display the content in relation to the real-worldasset, in conjunction with an XR workspace, as discussed below infurther detail. Additionally or alternatively, various playbooks,insights, predictions, annotations, and/or runbooks that include set ofcommands and/or simple logic trees (e.g., if-then-else) associated withan asset and possible actions (e.g., “if the operating temperature isabove 100 degrees Celsius, then show options for activating fans”) maybe implemented and/or presented to the user.

In some embodiments, in order to authenticate an instance of XRapplication 1814 and/or mobile operations application 1816 associatedwith a particular user and/or device 1804, 1810 XR application 1814and/or mobile operations application 1816 may present a uniqueidentifier associated with the user and/or device 1804, 1810 on adisplay device (e.g., on a display of host device 1804). The user maythen register the unique identifier with cloud-based data intake andquery system 306 and/or data intake and query system 108, such as byentering the unique identifier into a user interface (e.g., a webportal) associated with cloud-based data intake and query system 306 ordata intake and query system 108. In response, XR application 1814and/or mobile operations application 1816 may receive credentials thatcan be used to access real-time data outputted by data intake and querysystem 108. Additional queries transmitted by the authenticated device1804, 1810 to data intake and query system 108 may then implement thecredentials associated with the unique identifier. In this manner,secure, bidirectional communications may be established between a givendevice 1804, 1810 and data intake and query system 108.

Once the communications connection is established, a given user maycause a given device 1804, 1810 to acquire data associated with a givenasset. For example, during a remote collaboration session, host device1804 may scan a physical space that includes an asset. Host device 1804may retrieve a unique asset identifier (ID) that corresponds to theparticular asset. Once host device 1804 obtains the unique asset ID,host device 1804 transmits queries to data intake and query system 108requesting one or more values associated with the asset. Additionally oralternatively, remote device 1810 may receive the unique asset ID andtransmit separate queries to data intake and query system 108.

For example, host device 1804 may send a request for specific fieldvalues for the asset included in a given physical space. Host device1804 may include the unique asset ID in the request sent to data intakeand query system 108. In response, data intake and query system 108 mayretrieve events associated with the unique asset ID and may useextraction rules to extract values for fields in the events beingsearched, where the extracted values include the requested field values.Data intake and query system 108 may then transmit the field valuesassociated with the unique asset ID to host device 1804.

In various embodiments, data intake and query system 108 may transmitthe raw data retrieved from the field values included in the event data.Alternatively, data intake and query system 108 may filter, aggregate,or otherwise process the raw data prior to transmitting the fieldvalues. For example, in some embodiments, data intake and query system108 may generate a dashboard associated with the unique asset ID. Thedashboard may include a filtered subset of data values, where the subsetof data values is filtered based on additional criteria, such as userrole (e.g., a user role identifier value associated with the host user),location, type of device (e.g., whether host device 1804 is a smartphone, tablet, AR headset, etc.), and/or time.

XR application 1814 receives the field values from data intake and querysystem 108, where the field values represent the values of one or moremetrics associated with the unique asset ID. In an implementation, thefield values are extracted from fields that are defined post-ingestion(e.g., at search time), as has been previously described (e.g., with alate-binding schema). The field values transmitted by data intake andquery system 108 may be in any technically-feasible format.

In various embodiments, data intake and query system 108 generates adashboard that includes one or more visualizations of the underlyingtextual and/or numerical information based on the retrieved fieldvalues. In various embodiments, mobile operations application 1816 maydisplay one or more visualizations included in the dashboard receivedfrom data intake and query system 108. Additionally or alternatively, XRapplication 1814 may generate an XR workspace that includes one or moredisplay panels, where the one or more display panels include thevisualizations included in the dashboard as a portion of the XRworkspace. In some embodiments, the dashboard may also include a portionof the field values as a data set. In such instances, XR application1814 and/or mobile operations application 1816 may generatevisualizations based on the field values included in the data set.

In various embodiments, data intake and query system 108 may generate arunbook (e.g., a playbook) that includes set of commands and/or simplelogic trees (e.g., if-then-else) associated with an asset and possibleactions that may be implemented and/or presented to the user. In suchinstances, data intake and query system 108 may transmit the generatedrunbook to XR application 1814 and/or mobile operations application 1816in order to present and/or run commands associated with the asset orvisualizations associated with the asset. In some embodiments, dataintake and query system 108 may generate a context-sensitive runbookthat is only presented when a specific set of criteria are met. In suchinstances, XR application 1814 and/or mobile operations application 1816may only present the context-sensitive runbook upon all of the set ofcriteria being met.

FIG. 18B illustrates a more-detailed view of the example networkedcomputer environment 100 of FIG. 1 , in accordance with exampleimplementations. As shown, the networked computer environment 1801 mayinclude, without limitation, data intake and query system 108 and hostdevice 1804 communicating with one another over one or more networks420. Data intake and query system 108 and host device 1804 functionsubstantially the same as described in conjunction with FIGS. 1 and 4 ,except as further described herein. Examples of host device 1804 mayinclude, without limitation, a smartphone, a tablet computer, a handheldcomputer, a wearable device, a virtual reality (VR) console, anaugmented reality (AR) console, a laptop computer, a desktop computer, aserver, a portable media player, a gaming device, and so forth.

Host device 1804 may include, without limitation, processor 1802,storage 1807, input/output (I/O) device interface 1806, networkinterface 1808, interconnect 1811, and system memory 1812. System memory1812 includes extended reality (XR) application 1814, mobile operationsapplication 1816, and database 1818. Additionally, while networkedcomputer environment 1801 illustrates components of host device 1804,remote device 1810 may include one or more similar components to thosedescribed in relation to host device 1804.

In some implementations, host device 1804 may be a drone, or a deviceattached, e.g., mounted, to a drone or other carrier, and may beoperated remotely (either at the site of remote device 1810, or at aseparate site completely) or may operate autonomously. Suchimplementations may be useful where it is inconvenient or unsafe tobring a person to an actual site, e.g., in case of a biologicaloutbreak, or high likelihood of disease transmission, or presence offire or toxic gases, etc. In such implementations, host device 1804and/or the carrier may be operated by a user that is remote from hostdevice 1804, or host device 1804 may operate without user control, e.g.,programmed to carry out a specific task, with varying levels ofadaptability and specificity of the task dependent on the sophisticationof host device 1804 and/or a carrier of host device 1804, e.g., a drone.In some implementations, host device 1804 may be set to keep its sensorson, or toggled on and off, and then mounted to a carrier that iscontrolled remotely, e.g., a drone.

In general, processor 1802 may retrieve and execute programminginstructions stored in system memory 1812. Processor 1802 may be anytechnically-feasible form of processing device configured to processdata and execute program code. Processor 1802 may be, for example, acentral processing unit (CPU), a graphics processing unit (GPU), anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA), and so forth. Processor 1802 stores and retrievesapplication data residing in the system memory 1812. Processor 1802 isincluded to be representative of a single CPU, multiple CPUs, a singleCPU having multiple processing cores, and the like. In operation,processor 1802 is the master processor of client device 404, controllingand coordinating operations of other system components.

System memory 1812 stores software application programs and data for useby processor 1802. For example, system memory 1812 may include, withoutlimitation, extended reality application 1814, mobile operationsapplication 1816, and/or database 1818. Processor 1802 executes softwareapplication programs stored within system memory 1812 and optionally anoperating system. In particular, processor 1802 executes software andthen performs one or more of the functions and operations set forth inthe present application.

Storage 1807 may be a disk drive storage device. Although shown as asingle unit, the storage 1807 may be a combination of fixed and/orremovable storage devices, such as fixed disc drives, floppy discdrives, tape drives, removable memory cards, or optical storage, networkattached storage (NAS), or a storage area-network (SAN). Processor 1802communicates to other computing devices and systems via networkinterface 1808, where network interface 1808 is configured to transmitand receive data via one or more communications networks 420.

Interconnect 1811 facilitates transmission, such as programminginstructions and application data, between processor 1802, input/output(I/O) device interface 1806, storage 1807, network interface 1808, andsystem memory 1812. I/O device interface 1806 is configured to receiveinput data from user I/O devices. These I/O devices include, withoutlimitation, sensor(s) 1820 (e.g., one or more cameras, locationsensor(s), etc.), input device(s) 1822 (e.g., a keyboard, stylus,microphone, etc.), and/or a display device 1824. Display device 1824generally represents any technically-feasible means for generating animage for display. For example, display device 1824 may be a liquidcrystal display (LCD) display, organic light-emitting diode (OLED)display, or a digital light processing (DLP) display. Camera 1820acquires images via a lens and converts the images into digital form.The images acquired by the camera 1820 may be stored in storage 1807and/or system memory 1812. An acquired image may be displayed on thedisplay device 1824, either alone or in conjunction with one or moreother acquired images, graphical overlays, and/or other data.

Sensor(s) 1820 enable host device 404 to acquire sensor data that enableone or more applications to perform various operations. For example,sensor(s) 1820 may include location sensors that enable host device 1804to determine a specific physical location and/or orientation. In someembodiments, location sensor(s) 1820 may include a network-based sensorthat communicates with data intake and query system 108 via one or morenetwork(s) 420, which may be part of a production-monitoring network. Insome embodiments, location sensor(s) 1820 may include a network-basedsensor that communicates with one or more data intake and query systems108 via a local area network and/or a wide area network. In variousembodiments, the production-monitoring environment may include multipleassets and/or multiples devices 1804, 1810 each of which may communicatewith data intake and query system 108, and each of which is capable ofidentifying one or more assets within a real-world environment based onidentifier tags, geofences, and/or any other object-identificationtechnique disclosed herein.

In various embodiments, sensor(s) 1820 may include various imagingsensors that acquire sensor data relating to the real-world environment.For example, host device 1804 may include one or more cameras that scana given physical space and acquire two-dimensional (2D) data of surfacesof the physical space. Additionally or alternatively, sensor(s) 1820 mayinclude one or more depth cameras that scan the given physical space andacquire three-dimensional (3D) data of the physical space. In someembodiments, host device 1804 may process the 2D data and/or 3D data inorder to identify assets included in the physical space.

Input device(s) 1822 include one or more devices that receive inputsfrom the user and/or other devices. For example input device(s) 1822 mayinclude a microphone that acquires audio signals for storage andanalysis. Additional examples of I/O device(s) 1822 (not explicitlyshown) may include one or more buttons, a keyboard, and a mouse or otherpointing device. I/O device interface 1806 may also include an audiooutput unit configured to generate an electrical audio output signal,and the additional user I/O devices may further include a speakerconfigured to generate an acoustic output in response to the electricalaudio output signal.

FIG. 19 illustrates a block diagram of an example networked computerenvironment 1900, in accordance with example implementations. As shown,a networked computer environment 1900 may include, without limitation,host device 1804, one or more remote devices 1810, data processingservice 1902, remote storage 1904, tunnel bridge 1906, host extendedreality (XR) environment 1910, remote XR environment 1930, coupled toremote device 1810-1 and remote environment 1940 coupled to remotedevice 1810-2. Host device 1804 includes depth sensor 1924, imagingsensor 1926, and XR application 1814. Host XR environment 1910 includesreal-world asset 1912 and XR workspace 1914. Remote XR environment 1930includes rendered asset 1932 and XR workspace 1934. Remote environment1940 includes rendered asset 1942 and remote workspace 1944. Dataprocessing service 1902 includes workspace service 1908 and data intakeand query service 108.

In various embodiments, host device 1804 functions substantially thesame as client device 102, 404 described in conjunction with FIGS. 1 and4 , except as further described herein. Examples of host device 1804 mayinclude, without limitation, a smartphone, a tablet computer, a handheldcomputer, a wearable device, an XR console, a laptop computer, a desktopcomputer, a server, a portable media player, a gaming device, and soforth. In some embodiments, host device 1804 executes one or moreapplications that present, compute, or generate data based on datareceived from data processing service 1902. In some embodiments, hostdevice 1804 may include, without limitation, smartphones, tabletcomputers, handheld computers, wearable devices, laptop computers,desktop computers, servers, portable media players, gaming devices, anApple TV® devices, and so forth.

For example, host device 1804 may execute an extended reality (e.g.,augmented reality (AR), mixed reality (MR), and/or virtual reality (VR))application, which presents a portion of a real-world environment,performance metrics associated with assets in the real-worldenvironment, and/or other data as provided by data intake and querysystem 108 and/or data processing application 1902. In variousembodiments, XR application 1814 included in host device 1804 maygenerate host XR environment 1910 based on sensor data acquired fromsensor(s) 1820. For example, XR application 1814 may include imagingdata acquired by imaging sensor 1926 when generating host XR environment1910.

Sensor(s) 1820 includes various sensors that acquire data about thereal-world environment. In various embodiments, host device 1804 mayinclude sensor(s) 1820. For example, host device 1804 that includesdepth sensor 1924 and imaging sensor 1926. Additionally oralternatively, host device 1804 may control one or more sensor(s) 1820that are communicatively coupled to host device 1804. For example, hostdevice 1804 may be a desktop computer that controls the movement andoperation of one or more wireless cameras. In various embodiments,sensor(s) 1820 may include additional sensors, such as audio sensors,position sensors (e.g., an accelerometer and/or an inertial measurementunit (IMU)), motion sensors, and so forth.

Depth sensor 1924 includes one or more sensors that acquire sensor datarelating to the depth of objects within an environment. For example,depth sensor 1924 may be one or more (e.g., camera array) lightdetection and ranging (LiDAR) cameras that generate depth informationbased on reflected light. In various embodiments, depth sensor 1924generates depth sensor data using on or more depth imaging techniques,such as triangulation, structured light imaging, time-of-flight imaging,stereo imaging, laser scan, and so forth. In some embodiments, hostdevice 1804 may compute various depth properties of the environmentusing the sensor data and generate 3D depth data that includes thecomputed depth properties. Additionally or alternatively, host device1804 may transmit the 3D depth data to remote device(s) 1810 for furtherprocessing. In some embodiments, depth sensor 1924 may include one ormore infrared sensors, time-of-flight depth sensors, stereo depthsensors, audio depth sensors (e.g., RADAR sensors, sonograms, etc.), andso forth.

Imaging sensor 1926 includes one or more optical sensors, such as RGBcameras, infrared cameras, and/or camera arrays, which includes two ormore of such cameras. Other imaging sensors may include imagers and/orlasers sensors. In various embodiments, host device 1804 may generate 2Dsurface data from the image sensor data acquired by imaging sensor 1926.

In various embodiments, host device 1804 may provide updates to theimaging data (e.g., depth sensor data and image sensor data) associatedwith real-world environments. For example, host device 1804 may updatethe 2D surface data and the 3D depth data associated with a real-worldenvironment by re-scanning the real-world environments (e.g.,re-scanning every 10 seconds) using imaging sensor 1926 and depth sensor1924. Such re-scanning may be triggered in multiple ways. For example,host device 1804 may be triggered to attempt a rescan at periodicintervals (e.g., a setting to attempt a rescan every 20 seconds, every 5minutes. etc.), in response to a change in the location of host device1804, and/or in response to actions taken by remote device 1810 (e.g.,receiving a message requesting a re-scan). Additionally oralternatively, host device 1804 may receive a user input to rescan aportion of the environment. In such instances, host device 1804 mayacquire new depth sensor data from depth sensor 1924 and image sensordata from imaging sensor 1926 in response to the user input.

XR application 1814 included in host device 1804 acquires environmentaldata for the real-world environment for use in a remote collaborationsession. In various embodiments, XR application 1814 may receive thedepth sensor data and image sensor data and generate respective 3D depthdata and 2D surface data. In such instances, XR application 1814 maycombine correlated 3D depth data and 2D surface data to generate an XRstream and transmit the XR stream to one or more recipients (e.g.,remote devices 1810, remote storage 1904, etc.). In some embodiments, XRapplication 1814 may generate an adaptable 3D representation of thereal-world environment (e.g., determining coordinates corresponding tothe 3D depth data and/or 2D surface data and storing the set ofcoordinates as a scene) corresponding to a scene of the physical spaceand transmit the adaptable 3D representation to one or more recipients.

In some embodiments, host device 1804 may convert the adaptable 3Drepresentation of the real-world environments, included in the XRstream, into geometry data that represents the scene being shared byhost device 1804. In such embodiments, host device 1804 may convertcorrelated 3D depth data and 2D depth data into coordinate-based sets ofvertices, edges, and textures. Host device 1804 may then group the setsof vertices, edges, and textures into geometry data for other devices toreconstruct. In some embodiments, host device 1804 may set an anchorpoint as the origin of a scene and may determine coordinate data (e.g.,x-axis, y-axis, and z-axis coordinates) from the anchor point forportions of the 3D depth data and/or 2D surface data. Based on thedetermined coordinates, host device 1804 may convert the 2D surface dataand/or the 3D depth data into a set of vertices at specific coordinates,a set of faces comprising edges between pairs of vertices, and texturedata for specific coordinates.

For example, host device 1804 may convert determined coordinates forportions of 3D depth data included in the XR stream. Host device 1804may then use the coordinates to determine sets of triangles that formfaces of a combined mesh. A given triangle may have vertices defined ascoordinates that have the anchor point as an origin. Host device 1804may also convert the 2D surface data into one or more sets of textures,where a given texture corresponds to a specific face, by correlating theimage data from the image sensor to the depth data. In variousembodiments, remote device 1810 may render the scene by generatingmeshes that have faces formed by the triangles included in the geometrydata. Remote device 1810 may then apply textures to the correspondingfaces (e.g., fill a triangle with the texture that has the correspondingcoordinates). Various techniques of generating the portions of a scenefrom geometry data is described in further detail in pending U.S. PatentApplication entitled “MESH UPDATES IN AN EXTENDED REALITY ENVIRONMENT”(serial number unknown) filed on 30 Oct. 2020, and which is herebyincorporated by reference in its entirety for all purposes.

In some embodiments, XR application 1814 may generate the XR stream fora real-time remote collaboration session. In such instances, XRapplication 1814 may first determine a correlation between the 3D depthdata and the 2D surface data. In some implementations, the 2D surfacedata is clipped to match the 3D depth data, and the relative locationsof the 2D surface data and the 3D depth data are correlated such thatthe 2D surface data may be made into texture data that can be applied tothe 3D depth data. For example, XR application 1814 may determine that asection of the 2D surface data shares common coordinates with a sectionof the 3D depth data. Based on the correlation between the 3D depth dataand the 2D surface data, XR application 1814 may combine the 3D depthdata and the 2D surface data to generate the XR stream. Host device 1804may then transmit the XR stream via tunnel bridge 1906 to remote devices1810 for reproduction. In some embodiments, the XR stream includesrendered assets 1932, 1942, which are digital reproductions ofreal-world asset 1912 included in the real-world environment.Alternatively, remote device 1810-1 may use data included in the XRstream to generate rendered asset 1932 (e.g., a 3D model of real-worldasset 1912) in remote XR environment 1930, while remote device 1810-2may use data included in the XR stream to generate rendered asset 1942(e.g., a 2D model of real-world asset 1912) in remote environment 1940.

Host XR environment 1910 includes real-world asset 1912 and XR workspace1914. In various embodiments, XR application 1814 produces host XRenvironment 1910 for presentation via a display device associated withhost device 1804. For example, XR application 1814 may generate host XRenvironment 1910 and present a portion of host XR environment 1910 viadisplay device 1824. In such instances, host device 1804 may present atleast a portion of real-world asset 1912.

In some embodiments, XR application 1814 may generate an XR workspace1814 that includes one or more augmented reality (AR) elements. Such ARelements may include one or more display panels that are associated withportions of the environment. For example, real-world asset 1912 may beregistered with data processing service 1902 and may stream data to dataprocessing service 1902, where data processing service 1902 ingests andstores the data via data intake and query system 108. When generating XRworkspace 1914, XR application 1814 may send one or more queries toreceive data values, dashboards, playbooks, and so forth, to presentwithin XR workspace 1914. Any AR elements may be included, such aspanels that display the content (e.g., schemas, dashboards, cards,and/or visualizations generated from the extracted field values),directional indicators (e.g., pointers at the edge of the display deviceindicating the position of portions of the AR workspace relative to theposition and/or orientation of imaging sensor 1926). Other example ARelements include a full graphics overlay, a partial graphics overlay,text data (e.g., alerts, runbooks, playbooks, etc.), numerical data,color information, and/or icon data (e.g., a skull-and-crossbones symbolindicating that a machine that has failed), and/or one or more virtualobjects.

In various embodiments, XR workspace 1914 may include variouscollaboration tools that enable the user to interact with portions ofthe host XR environment 1910. For example, XR workspace 1914 may includegraphical pointers that highlight a small portion of the host XRenvironment 1910. Other collaboration tools may include, for example, acolor palette, a map of host XR environment 1910, a set of highlightpins, and so forth. In some embodiments, XR application 1814 may providea specific set of AR elements based on the user of host device 1804. Forexample, a user may be associated with a user identifier, roleidentifier and/or other criteria that correspond to a specific XRworkspace 1914. In such instances, XR application 1814 may generate XRworkspace 1914 based on the specific criteria associated with the hostuser.

Remote device(s) 1810 (e.g., 1810-1, 1810-2, etc.) functionssubstantially the same as client device 102, 404 described inconjunction with FIGS. 1 and 4 , except as further described herein.Examples of remote device 1810 may include, without limitation, asmartphone, a tablet computer, a handheld computer, a wearable device,an XR console, a laptop computer, a desktop computer, a server, aportable media player, a gaming device, and so forth. In someembodiments, host device 1804 executes one or more applications thatpresent, compute, or generate data based on data received from dataprocessing service 1902. In some embodiments, host device 1804 mayinclude, without limitation, smartphones, tablet computers, handheldcomputers, wearable devices, laptop computers, desktop computers,servers, portable media players, gaming devices, an Apple TV® devices,and so forth. It is noted that “remote” in this context means located ata different location, relative to host device 1804. In variousembodiments, what constitutes “remote” may differ based on the use case,but no minimum or maximum distance is specified here or required. Theremay be implementations in which remote device 1810 is in the next roomfrom the host device, or implementations where the remote device isthousands of miles away.

In various embodiments, remote device 1810 may generate a digitalreproduction of a portion of the real-world environment based on dataincluded in the XR stream provided by host device 1804. In someembodiments, remote device 1810 may receive the XR stream as a set ofserialized chunks that are below a specific data size (e.g., below 1MB). In such instances, remote device 1810 may combine the set ofserialized chunks into the XR stream and retrieve data from the XRstream.

Remote device 1810-1 executes an instance of XR application 1814 togenerate remote XR environment 1930. In various embodiments, XRapplication 1814 may extract the 3D depth data and the 2D surface dataincluded in the XR stream and may generate an adaptable 3Drepresentation of the real-world environment. The adaptable 3Drepresentation corresponds to the scene of the physical space and mayinclude rendered asset 1932, which is a digital representation ofreal-world assets 1912 that is included in the real-world environment.XR application 1814 may also generate an XR workspace 1934 within remoteXR environment 1930. In some embodiments, remote device 1810-1 may alsorender one or more portions of a background that correspond to areasproximate to real-world asset 1912 (e.g., the texture of the floorsurrounding real-world asset 1912).

While in some embodiments, XR workspace 1934 may be the same as XRworkspace 1914, in other embodiments, XR workspace 1934 may bedifferent. For example, in some embodiments, XR workspace 1934 mayinclude different AR elements than AR elements included in the XRworkspace 1914 included in the host XR environment 1910. For example, atechnician may operate host device 1804, where XR workspace 1914includes a first set of display panels, while an expert may operateremote device 1810-1, where XR workspace 1934 includes a second set ofdisplay panels. XR workspaces 1914, 1934 may differ based on a number ofdifferent factors, for example, XR workspaces 1914, 1934 may be adjustedbased on a type of remote device 1810-1, or based on a user or a profileof a user that operates the various devices 1804 and 1810. For example,a local technician operating host device 1804 may not have sufficientaccess to see certain XR environment information, so those informationalelements may be blocked or otherwise not shown in XR workspace 1914, butmay be visible to a higher-privileged user or user role of a userworking in XR workspace 1934 in remote device 1810.

In other implementations, the system may be designed to simplify theinformation displayed in XR workspace 1914 to more clearly illustratethe instructions and information given to the user of host device 1804.In some embodiments, such access control and XR environment decisionsmay be made by data processing service 1902. In other embodiments, dataprocessing service 1902 may send the same environments to host devices1804 and 1810, and the respective XR applications 1814 of host device1804 and 1810 may perform some processing to determine what elements todisplay in XR workspaces 1914 and 1934, respectively. In still otherembodiments, it may be a combination of data processing serviced 1902and extended reality applications 1814, working in concert at varioustimes, to determine the specific layouts and elements of XR workspaces1914 and 1934. In various embodiments, remote device 1810-1 may generateremote XR environment 1930 in real time.

In various embodiments, XR application 1814 may provide controls thatenable the remote user to navigate remote XR environment 1930independent of the position of host device 1804. In various embodiments,host device 1804 may initially scan a portion of the real-worldenvironment that includes real-world asset 1912. Host device 1804 mayprovide the 3D depth data and 2D surface data in the XR stream, where XRapplication 1814 generates a scene that is a digital reproduction of thescanned portion. Upon generating the scene, XR application 1814 mayrespond to navigation inputs (e.g., physically moving remote device1810-1, applying navigation inputs) by changing the viewing position ofremote device 1810-1 within the remote XR environment 1930 andpresenting a different portion of the scene.

In various embodiments, XR application 1814 in remote device 1810 mayreceive data from the XR stream based on host device 1804 rescanning atleast a portion of the real-world environment. In such instances, XRapplication 1814 may update the remote XR environment 1930 byre-generating the corresponding portion of the digital representation ofthe updated portion while maintaining the digital representation of thenon-updated portions. In some embodiments, XR application 1814 may firstcompare the differences between the existing scene and the updated 2Dsurface data and 3D depth data and may only regenerate the scene uponthe determining that the changes are above a threshold level.

In some embodiments, XR application 1814 may generate rendered asset1932 by combining portions of the 3D depth data and 2D surface dataincluded in the XR stream. For example, XR application 1814 may apply aportion of the 2D surface data as a texture to a corresponding portionof the 3D depth data. In some embodiments, XR application 1930 mayseparate rendered asset 1932 from other portions of the scene.Alternatively, remote device 1810-1 may combine the 3D depth data and 2Dsurface data included in the XR stream. In such instances, XRapplication 1814 may generate the scene without specifically identifyingrendered asset 1932.

XR workspace 1934 includes one or more augmented reality elements, suchas dashboards and other interaction and/or collaboration tools. Invarious embodiments, when generating XR workspace 1934, XR application1814 included in remote device 1810-1 may send one or more queries (viatunnel bridge 1906) to receive data values, dashboards, playbooks, andso forth, to present within XR workspace 1934. Additionally oralternatively, XR workspace 1934 may include various collaboration toolsthat enable the remote user to interact with portions of the remote XRenvironment 1810. In such instances, XR application 1814 may transmitdata to data processing service 1902 and/or host device 1804 based onthe identified interaction.

In one example, the remote user may use the color palette interactiontool to change the color of a specific portion of rendered asset 1932(e.g., changing the color of a non-functioning fan). XR application 1814may send data to host device 1804 that causes the host XR workspace 1914to add highlighted region 2816 over the corresponding portion ofreal-world asset 1912. In another example, XR application 1814 mayreceive an input that requests updated data for a display panel includedin XR workspace 1934. XR application 1814 may then respond to the datarequest by sending a query to data processing service 1902 thatprocesses the data query via data intake and query service 108.

In some embodiments, remote device 1810 may generate an environment forremote collaboration other than an extended reality environment. Forexample, a remote user may use a web browser to view a two-dimensionalreproduction of the real-world environment. In such instances, remotedevice 1810-2 may execute a different application (e.g., mobileoperations application, a web browser, a VR application, etc.) to view anon-XR environment. In some embodiments, remote device 1810-2 maygenerate a non-XR remote environment 1940 while remote device 1810-1generates remote XR environment 1930. In some such environments, inwhich remote device 1810-2 is a non-XR environment, the XR environmentthat would have been displayed may be translated into a non-XRenvironment to the extent possible, e.g., multimedia files may bedisplayed as popups, text may be displayed as tooltips, runbooks may beexecuted on mouse button click, or any combination thereof.

In various embodiments, mobile operations application 1816 may enable adifferent remote user to navigate and interact with remote environment1940 independent of host device 1804 and/or remote device 1810-1. Forexample, after mobile operations application 1816 generates remoteenvironment 1940, mobile operations application 1816 may providenavigational controls that enable the remote user to change the positionof remote device 1810-2 within remote environment 1940 independent ofthe position of host device 1804.

Mobile operations application 1816 generates remote environment 1940,where remote environment 1940 includes rendered asset 1942 and remoteworkspace 1944. In some embodiments, rendered asset 1942 may have adifferent format than rendered asset 1932. For example, rendered asset1932 may be a 3D mesh representation of real-world asset 1912, whilerendered asset 1942 may be a 2D photograph or icon of real-world asset1912, or a flattened version of the 3D mesh representation of real-worldasset 1912. Mobile operations application 1816 also generates remoteworkspace 1944. In some embodiments, remote workspace 1944 includesdisplay panels and/or interaction tools within an adaptable 3Drepresentation of the real-world environment. Alternatively, remoteworkspace may include display panels and/or interaction tools that arepositioned outside the adaptable representation. For example, remoteenvironment 1940 may present the adaptable representation of thereal-world environment in a first window, and present one or moredisplay panels, displaying data associated with the real-world asset, ina second window.

Data processing service 1902 processes data associated with a remotecollaboration session. In various embodiments, data processing asset2002 includes a workspace service 1908 that manages actions performed byhost device 1804 and/or remote device(s) 1810 in relation to the remotecollaboration session. Data intake and query system 108 manages theretrieval and transmission of data values associated with one or morereal-world assets 1912 included in the real-world environment.

In various embodiments, data processing service 1902 receives data fromone or more real-world assets 1912. In some embodiments, data processingservice 1902 may be a data ingestion application, such as a data streamprocessor (DSP) that ingests sensor data transmitted by one or morereal-world assets 1912 in real time. In such instances, data processingservice 1902 may generate a processed data set and transmit theprocessed data set to data intake and query system 108 for storage inone or more data stores. Additionally or alternatively, data processingservice 1902 responds to data requests by retrieving field values viathe data intake and query system 108, where the field values representthe values of one or more metrics associated with a particularreal-world asset 1912. In an implementation, the field values areextracted from fields that are defined post-ingestion (e.g., at searchtime), as has been previously described (e.g., with a late-bindingschema). The field values transmitted by data intake and query system108 may be in any technically-feasible format.

In various embodiments, data intake and query system 108 generates adashboard that includes one or more visualizations of the underlyingtextual and/or numerical information based on the retrieved fieldvalues. In various embodiments, the dashboard may present in one or morevisualizations included in the dashboard received from data intake andquery system 108. One or more workspaces 1914, 1934, 1944 include one ormore display panels that present the visualizations included in thedashboard. In some embodiments, the dashboard may also include a portionof the field values as a data set. In such instances, XR application1814 and/or mobile operations application 1816 may generatevisualizations based on the field values included in the data set.

Workspace service 1908 manages actions performed by host device 1804and/or remote device(s) 1810 in relation to the remote collaborationsession. In various embodiments, one or more remote devices 1810 mayregister or subscribe to receive the XR stream generated by host device1804 for a given remote collaboration session. In such instances,workspace service 1908 may cause data chunks corresponding to the XRstream to be transmitted to subscribed recipients. Additionally oralternatively, workspace service 1908 may cause the XR stream and/orother interaction data (e.g., recorded audio, actions performed by hostdevice 1804 and/or remote device(s) 1810) to be stored in remote storage1904. In some embodiments, workspace service 1908 may send notificationsto a target recipient (e.g. remote device 1810-1) to join a remotecollaboration session.

Remote storage 1904 includes one or more data stores that storeinformation associated with a remote collaboration session. In variousembodiments, host device 1804 may transmit the XR stream to remotestorage 1904, where remote storage stores the XR stream as a combinationof 2D surface data and 3D depth data. In such instances, the stored 2Dsurface data and 3D depth data is much smaller in data size than anequivalent high-definition video clip. Remote storage 1904 may alsostore other data associated with the XR stream. For example, remotestorage 1904 may receive and store one or more activity logs from hostdevice 1804 and/or remote devices 1810. In some embodiments, remotestorage 1904 may store an initial set of data chunks and subsequent setsof update chunks. In such instances, subsequent update data chunks mayoverwrite previous update chunks.

In some embodiments, a device may subsequently download data from remotestorage 1904 and may replay the remote collaboration session. In suchinstances, the device may navigate the adaptable 3D representation ofthe real-world environment, as well as view actions performed byparticipants, e.g., as recorded in the activity log. For example, aquality assurance technician may replay the remote collaboration sessionto determine how experts explained an issue during a recorded remotecollaboration session by viewing how an expert, e.g., a user operatingremote device 1810-1, identified an issue and used the interaction toolsto notify other participants during the recorded remote collaborationsession.

In some embodiments, the XR workspace 1934 may be stored as part of theremote collaboration session. In other embodiments, when the remotecollaboration session is played back, the system may contact dataprocessing service 1902 and re-generate the XR workspace 1934 as part ofthe playback. In such implementations, XR workspace 1934 may show thedata, e.g., data from dashboards, as it appeared when the remotecollaboration session was created. In other implementations, XRworkspace 1934 may show a same or similar XR workspace 1934 as when theremote collaboration session was created, but updated with all orpartial new data from the time that the remote collaboration session isplayed back. These features can be toggled or changed when the remotecollaboration session is played back, depending on the use case, forexample, some use cases may want to see the XR workspace 1934 data thesame as when the session was created, e.g., for security incidents orother instances where the session is to be replayed exactly or storedfor audit purposes. In other use cases, the remote collaboration sessionmay show updated data, such as when an unskilled technician wants toview the session with updated data, e.g., to check on the status of anindustrial IoT (Internet of Things) environment, such as a factoryfloor, assembly line, or any other connected workspace, e.g., an airportsecurity area.

Tunnel bridge 1906 is a device that establishes communications with oneor more devices included in the networked computer environment 1900. Forexample, tunnel bridge 1906 may establish one or more WebSocketconnections with host device 1804, remote devices 1810, data processingservice 1902, and/or remote storage 1904. In some embodiments, tunnelbridge 1906 may be a trusted service that establishes trust with one ormore devices in order to establish secure WebSocket connections withsuch devices. In some embodiments, tunnel bridge 1906 may performauthentication operations with other devices in order to establishtrust, and may then establish secure communications channels with theother devices, where tunnel bridge 1906 and/or other devices andtransmit secure communications using the secure communications channels.

In some embodiments, tunnel bridge 1906 enables E2EE communicationsbetween two separate devices by forwarding one or more encrypted datapackets without fully decrypting the encrypted data packet. For example,tunnel bridge 1906 may receive an encrypted data packet that wasencrypted and signed using multiple encryption keys. The trusted tunnelbridge 1906 may determine whether the encrypted data packet was validlysigned with one of the encryption keys without decrypting the encrypteddata packet.

FIG. 20 illustrates an example extended reality environment thatpresents information using the networked computer environment, inaccordance with example implementations. As shown, remote collaborationsystem 2010 includes asset 2011, XR workspace 2030, and dashboard 2050.Asset 2011 includes tag 2012 that includes unique asset ID 2014 at tagposition 2013. XR workspace 2030 includes a plurality of panels 2031(e.g., 2031-1, 2031-2) and/or annotation panel(s) 2061. Dashboard 2050includes visualization set 2052, data set 2054, and a copy of uniqueasset ID 2014-2.

In various embodiments, XR application 1814 may identify a given asset2011 that has been registered with data processing service 1902, wheredata intake and query service 108 ingests and stores sets of data valuesassociated with the asset (e.g., performance metrics associated with theoperation of asset 2011). In some embodiments, XR application 1814generates XR workspace 2030 within a given environment (e.g., host XRenvironment 1910, remote XR environment 1930, etc.) and presents variousimages in one or more panels 2031 within XR workspace 2030 on displaydevice 1824 of host device 1804 and/or remote device 1810.

Each of the panels 2031, 2061 is positioned within XR workspace 2030relative to tag position 2013. In some embodiments, XR application 1814may scan tag 2012 in order to receive XR workspace 2030 and/or dashboard2050 from data intake and query system 108. In other implementations, XRapplication 1814 may respond to scanning tag 2012 by requesting one ormore field values associated with unique asset ID 2014 encoded in thetag 2012 from data intake and query system 108. In such instances, XRapplication 1814 responds to receiving the field values by generating XRworkspace 2030 and/or dashboard 2050 from the one or more received fieldvalues. Dashboard 2050 includes visualization set 2052 (e.g., 2052-1,2052-2, etc.) and/or data set 2054. Dashboard 2050 provides one or morevisualizations included in visualization set 2052 to present within oneor more panels 2031 of XR workspace 2030.

Extended reality (XR) workspace 2030 includes one or more portions ofgraphics overlays (e.g., panels 2031 and/or annotation panel 2061)and/or indicators within the extended reality environment. In variousembodiments, XR workspace 2030 may include a full graphics overlay, or apartial graphics overlay. Additionally or alternatively, portions of XRworkspace 2030 may include text data, numerical data, color information,and/or icon data (e.g., a skull-and-crossbones symbol indicating that amachine has failed). For example, a portion of an overlay within XRworkspace 2030 may include a highlighted portion, signifying informationof particular interest to the user.

In some embodiments, the field values provided by data intake and querysystem 108 may include only the underlying textual and/or numericalinformation. In such instances, XR application 1814 may generategraphical overlays locally based on the underlying textual and/ornumerical information. In various embodiments, the one or more overlayswithin XR workspace 2030 may be static, or may be dynamically updated.For example, panel 2031-1 may include visualization 2052-2 thatillustrates a total data load in relation to the operation of asset2011. In this instance, XR application 1814 may send multiple requeststo data intake and query system 108 while viewing XR workspace 2030 inorder to receive updated field values. XR application 1814 maydynamically update visualization 2052-2 based on the updated fieldvalues received from data intake and query system 108. In someimplementations, one or more overlays may include interactive hooks toallow an operator of the system to interact with the one or moreoverlays.

Although various embodiments disclosed herein are described inconjunction with extended-reality techniques (e.g., generating XRoverlays), each extended-reality technique also may be implemented in anon-XR environment. Further, specific XR techniques (e.g.,virtual-reality techniques, augmented-reality techniques, etc.)disclosed herein also may be implemented in other environments. Forexample, for clarity of explanation, various embodiments disclosedherein are described in conjunction with AR overlays (e.g., fieldvalues, images, dashboards, cards, etc.). However, each of theseembodiments may also be implemented by generating such overlays in a VRenvironment. Accordingly, the term extended reality (XR) may be used torefer to techniques that can be performed in an AR environment, a VRenvironment, and/or any combination thereof.

In various embodiments, XR application 1814 superimposes XR workspace2030 onto the image(s) acquired via camera 1820. For example, one ormore panels 2031-1, 2031-2 and/or annotation panel 2061 may be overlaidat positions relative to tag position 2013 corresponding to tag 2012,such as next to tag position 2013 and/or in front of tag position 2013.XR application 1814 causes the images superimposed with XR workspace2030 to be presented on display device 1824.

In some embodiments, XR application 1814 may cause XR workspace 2030 tobe presented on display device 1824 without presenting the acquiredimage. In general, XR application 1814 superimposes portions of XRworkspace 2030 based on any of one or more determined dimensions and/orpositions of asset 2011, the known size of tag 2012, thethree-dimensional location and/or orientation of tag position 2013, andthe detected plane of tag position 2013. In some embodiments, XRapplication 1814 places portions of XR workspace 2030 over portions ofthe adaptable 3D environment. For example, XR application 1814 maysuperimpose portions of XR workspace 2030 over portions of renderedasset 1932.

In some embodiments, XR application 1814 may receive additionalinformation from data intake and query system 108 and may present theadditional information on display device 1824. This additionalinformation may be in any technically-feasible format. For example, dataintake and query system 108 may transmit content (e.g., various schemas,dashboards, cards, playbooks, runbooks, and/or visualizations) to XRapplication 1814. The contents include data, including real-time data(e.g., near real-time data) associated with asset 2011 retrieved by dataintake and query system 108 based on unique asset ID 2014. XRapplication 1814 may then display the content in conjunction with thereal-world asset 2011 using XR workspace 2030.

For example, XR application 1814 may request data relating to asset 2011by generating a data request that includes unique asset ID 2014-1 andsending the data request to data intake and query system 108. XRapplication 1814 may then receive dashboard 2050 from data intake andquery system 108 that includes data set 2054, which includes one or morefield values retrieved by data intake and query system 108 in responseto the data request. In some embodiments, dashboard 2050 may includevarious insights, predictions, and/or annotations associated with asset2011. For example, data intake and query system 108 may employ variousmachine-learning (ML) algorithms to generate one or more predictionsassociated with field values included in data set 2054. Additionally oralternatively, dashboard 2050 may include one or more annotations 2062provided by one or more users in relation to asset 2011. Data intake andquery system 108 may associate the one or more annotations 2062 to asset2011 and store the one or more annotations 2062. Data intake and querysystem 108 may then include the one or more annotations 2062 as aportion of data set 2054.

In various embodiments, dashboard 2050 also includes visualization set2052 that includes one or more visualizations relating to portions ofdata set 2054. In some embodiments, dashboard 2050 may includevisualizations associated with asset ID 2014-2 that are presented whenthe asset ID 2014-2 is provided as an input into the dashboard 2050. Insuch instances, dashboard 2050 may automatically use asset ID 2014-2 asan input (e.g., a form input into a schema) to generate one or morevisualizations included in visualization set 2052. In variousembodiments, each visualization corresponds to applicable portions ofdata set 2054. For example, a portion of data set 2054 that correspondsto field values for a specified time range may have a correspondingtimeline graph visualization. Similarly, a set of notification messagesincluded in data set 2054 may have a corresponding set of notificationvisualizations.

In some embodiments, visualization set 2052 includes one or moreannotations 2062 previously generated by one or more users. For example,by pointing camera 1820 at tag 2012, XR application 1814 obtains uniqueasset ID 2014-1 of “10245” from tag 2012, and sends a request to dataintake and query system 108 that includes unique asset ID 2014-1. Hostdevice 1804 and/or remote device 1810 may receive dashboard 2050 thatincludes visualizations 2052-1, 2052-2, 2052-3 based on field valuesassociated with asset 2011 for a specific time period. Host device 1804and/or remote device 1810 may also receive the corresponding XRworkspace 2030. XR workspace 2030 associated with asset 2011 may includemultiple panels 2031-1, 2031-2 and/or annotation panel(s) 2061positioned relative to tag position 2013. XR application 1814 thenpresents XR workspace 2030 via display device 1824, where panels 2031,2061 of XR workspace 2030 include the visualizations included invisualization set 2052.

In various embodiments, after generating XR workspace 2030, along withany visualizations 2052 and/or annotations 2062 included in panels 2031,2061, onto the image(s) acquired via camera 1820, XR application 1814may store the enhanced image in an enhanced image data store included instorage 1807 and/or in system memory 1812. In some embodiments, theenhanced image data store may be stored within database 1818. In someembodiments, XR application 1814 generates and populates XR workspace2030 onto a VR scene rather than onto an image acquired from camera1820. In such instances, the images stored in the enhanced image datastore represent VR images augmented with AR overlays, rather thanacquired images augmented with AR overlays.

FIGS. 21A-F are example user interfaces for a host user initiating aremote collaboration session via host device 1804, in accordance withexample implementations. In general, host device 1804 scans a physicalspace within a real-world environment. XR application 1814 receives 2Dsurface data and 3D depth data of the physical space and generates an XRstream that host device 1804 transmits to one or more recipients. Invarious embodiments, host device 1804 may receive a selection of one ormore recipients that are to participate with host device 1804 in aremote collaboration session. In such instances, the selected recipientseach receive the XR stream produced by host device 1804. During setup ofthe remote collaboration session, XR application 1814 generates variousviews 2100, 2110, 2120, 2130, 2140, 2150 on host device 1804.

As shown in FIG. 21A, view 2100 presents a menu with selectable icons toconduct various operations associated with the real-world environmentand/or data processing service 1902. Such icons include remotecollaboration icon 2102, non-AR dashboard history icon 2103, nearbydashboards icon 2104, settings icon 2105, start a new scan icon 2106,and cancel icon 2107. In various embodiments, XR application 1814 maypresent view 2100 to provide a host user with options to scan a physicalspace within the real-world environment (e.g., selecting remotecollaboration icon 2102, settings icon 2105, or start a new scan icon2106) and/or perform various operations associated with dashboardsprovided by data processing service 1902 (e.g., selecting non-ARdashboard history icon 2103 or nearby dashboards icon 2104).

In various embodiments, upon receiving a user input corresponding to auser selection of remote collaboration icon 2102, XR application 1814may respond by presenting view 2110. As shown in FIG. 22B, view 2110illustrates a menu with selectable icons to initiate a remotecollaboration. Such icons include a share my screen icon 2112, share myenvironment icon 2114, and cancel icon 2116. In some embodiments, hostdevice 1804 may respond to a selection of share my screen icon 2112 byinitiating a remote collaboration session by sharing the screen of hostdevice 1804.

In various embodiments, host device 1804 may respond to a selection ofshare my environment icon 2114 by determining whether host device 1804has recently scanned a physical space (e.g., performed a scan inresponse to a selection of the start a new scan icon 2106). In suchinstances, host device 1804 may prepare the recent scan for sharing bygenerating an XR stream that includes 2D surface data and 3D depth datafrom the recent scan. Alternatively, when host device 1804 determinesthat host device 1804 has not performed a recent scan, XR application1814 proceeds by presenting view 2120.

Although not required in order to enable remote collaboration, in someembodiments, the host device 1804 may provide prompts to the user toscan more of the physical space, to enable the remote device to viewdifferent areas of the physical space at the remote location. Forexample, as shown in FIG. 21C, XR application 1814 presents view 2120 toscan a physical space within a real-world environment. View 2120includes prompt 2122, scanning region 2124, and scanned region(s) 2126(e.g., 2126-1, 2126-2, etc.). In some embodiments, XR application 1814may provide prompt 2122 that instructs a host user to scan a particularasset or collection of assets within the physical space (e.g., scanninga particular server device). Alternatively, XR application 1814 mayprovide prompt 2122 that instructs the host user to scan a region of thereal-world environment (e.g., one or more portions of an open space,building, room, etc.).

In various embodiments, XR application 1814 may present view 2120 whilehost device 1804 scans a physical space. For example, XR application1814 may present scanning region 2124 that corresponds to a specificregion or a specific object within the physical space. When XRapplication 1814 determines that a portion of the physical space hasbeen successfully scanned, XR application 1814 presents one or morescanned regions 2126. In some embodiments, XR application 1814 may lockthe scanned region and store 3D texture data and/or 2D surface data forthe scanned region while host device 1804 scans other portions of thephysical space. In some embodiments, the host user may change theposition of host device 1804 in order to scan other regions of thephysical space. Alternatively, XR application 1814 may expand a givenscanned region as host device 1804 changes position (e.g., the host usermoves and/or rotates host device 1804).

As shown in FIG. 21D, view 2130 includes prompt 2132, scanning region2124, scanned regions 2126, and invitation icon 2134. As XR application1814 determines that a set of scanned regions 2126 have beensuccessfully scanned, XR application 1814 may update prompt to specifythat a sufficient amount of the physical space has been scanned to sharein a remote collaboration session. In some embodiments, prompt 2132 mayalso specify that the scanned region may be updated during the remotecollaboration session. When XR application determines that enough of thephysical space has been successfully scanned, XR application 1814 mayprovide invitation icon 2134 to invite others to the remotecollaboration session.

As shown in FIG. 21E, view 2140 displays an invitation menu forpotential participants in the remote collaboration session. View 2140includes invitation menu 2442, selected participants 2144 (e.g., 2144-1,2144-2, etc.), and invitation icon 2146. Once the host user selects oneor more participants for the remote collaboration session, the host userselects invitation icon 2146. XR application 1814 responds to theselection of the invitation icon by retrieving address information foreach of the selected participants 2144 and sending invitations to eachof the selected participants.

As each selected participant accepts the invitation, host device 1804may receive an indication of a remote device 1810 used by the selectedparticipant. In some embodiments, XR application 1814 may identify eachremote device 1810 as an intended recipient of the XR stream and maysend separate messages to each respective recipient. Additionally oralternatively, each selected participant may register with workspaceservice 1908 included in data processing service 1902. In suchinstances, data processing service 1902 may direct the XR stream to eachintended recipient during the remote collaboration session.

As shown in FIG. 21F, view 2150 includes host XR environment portion2152, asset 2154, and record icon 2156. In various embodiments, XRapplication 1814 included in host device 1804 presents view 2150 duringa remote collaboration with one or more remote devices 1810.

Host XR environment portion 2152 corresponds to a view of host XRenvironment 1910 based on a position of host device 1804 relative to thephysical space. In some embodiments, host XR environment portion 2152includes one or more assets 2154. In such instances, XR application 1814may highlight asset 2154 within host XR environment portion 2152 mayidentify asset 2154. Additionally or alternatively, XR application 1814may display one or more display panels, including various dashboards,playbooks, and so forth, within host XR environment 2152.

In various embodiments, host device 1804 may change position within thereal-world environment. In such instances, XR application 1814 mayupdate host XR environment portion 2152 to reflect the position changeof host device 1804. Additionally or alternatively, the position changeof host device 1804 does not modify the view seen by the remoteparticipants via the one or more remote devices 1810.

In some embodiments, XR application 1814 may present record icon 2156.In such instances, XR application 1814 may respond to a selection ofrecord icon 2156 by recording the remote collaboration session.Recording the remote collaboration session may include storing the XRstream, an activity log of actions performed by host device 1804 and/orthe one or more remote devices 1810, and/or AR elements displayed byhost device 1804 and/or the one or more remote devices 1810 within therespective host XR environment 1910, or remote XR environment 1930,and/or remote environment 1940. In some embodiments, XR application 1814may also store an audio recording from each respective device includedin the remote collaboration session, or other additional annotation orinformation, as is relevant to the particular use case.

FIGS. 22A-D are example user interfaces for a remote user joining aremote collaboration session via remote device 1810, in accordance withexample implementations. In general, remote device 1810 joins a remotecollaboration session, where host device 1804 shares a screen or anenvironment with remote device 1810. Remote device 1810 presents views2200, 2210, 2220, 2230 to enable the remote user to join the remotesession as a participant and view portions of a physical space scannedby host device 1804. In various embodiments, remote device 1810 maynavigate a representation of the physical space independent of the hostdevice 1804.

As shown in FIG. 22A, view 2200 presents a prompt to join a remotesession. View 2200 includes home screen 2202, prompt 2204, and link2206. In various embodiments, remote device 1810 may receive aninvitation to join a remote collaboration session. In such instances,remote device 1810 may present prompt 2204 indicating that the remoteuser has been invited to join a remote collaboration session as aparticipant. In such instances, prompt 2204 may include link 2206 tojoin the remote collaboration session. In some embodiments, link 2206may cause remote device 1810 to register with workspace service 1908 asa participant for the specific remote collaboration session. Uponregistration, workspace service 1908 may direct the XR stream for theremote collaboration session, as generated by host device 1804, toremote device 1810.

As shown in FIG. 22B, view 2210 presents a splash page describing theremote collaboration session. View 2210 includes loading progress bar2212 and instructional panel 2214. In operation, XR application 1814included in remote device 1810-1 receives the XR stream and generatesremote XR environment 1930 that includes an adaptable 3D representationof the physical space. In some embodiments, XR application 1814 maypresent view 2210 while initially generating the remote XR environment1930 by rendering the adaptable 3D representation.

View 2210 presents a progress bar based on XR application generatingremote XR workspace 1930. In some embodiments, generating remote XRworkspace 1930 includes rendering the adaptable 3D representation of thephysical space and separately transmitting requests for data associatedwith one or more assets included in the adaptable 3D representation(e.g., asset 2154). Additionally or alternatively, view 2210 may includeinstructional panel 2214. Instructional panel 2214 provides informationabout the remote collaboration session. For example, instructional panel2214 may instruct the remote user to be mindful of the surroundings ofat the remote location when moving remote device 1810-1. Instructionalpanel 2214 may also include instructions about using one or morecollaboration tools (e.g., graphical pointers, pins, highlighters, etc.)that the remote user can implement to navigate within remote XRenvironment 1930 and/or interact with the rendered asset within remoteXR environment 1930.

As shown in FIG. 22C, view 2220 presents remote XR environment portion2232. View 2220 includes remote XR environment portion 2152 and renderedasset 2234. Remote XR environment portion 2232 corresponds to a view ofremote XR environment 1930 based on a position of remote device 1810-1.Remote XR environment 1930 renders the XR stream, corresponding to thescene scanned by host device 1804, as an adaptable 3D representation ofthe physical space.

In some embodiments, remote device 1810-1 may determine an anchorposition for the scene and may render portions of the scene relative tothe anchor position. In such instances, XR application 1814 included inremote device 1810-1 may determine the position of remote device 1810-1relative to the anchor position in order to determine the position ofremote device 1810-1 relative to portions of the scene. Upon determiningthe position of remote device 1810-1 relative to the portions of thescene, XR application 1814 may present remote XR environment portion2232 to reflect the position of remote device 1810-1. In someembodiments, an XR representation of remote device 1810-1 may appear inthe host XR environment 1910 of the host device 1804. For example, ifremote device 1810 is a cellular phone of a particular brand, a 3D modelrepresenting that cellular phone brand may be rendered in host XRenvironment 1910 according to the position of remote device 1810relative to rendered asset 1932. In other implementations, remote device1810 may be represented by a symbol or simple object, such as a cube orsphere. In still other implementations, remote device 1810 may berepresented as a line or ray, with the line or ray pointing in thedirection representing the orientation of the remote device 1810relative to the rendered asset 1932.

For example, XR application 1814 may determine that, based on the anchorposition for the scene, remote device 1810-1 is at a position andorientation that is to the right of rendered asset 2234. XR applicationmay then respond by generating remote XR environment portion 2232 toreflect that position of remote device 1810-1 relative to rendered asset2234.

In various embodiments, remote device 1810-1 may change position withinthe real-world location of remote device 1810-1. In such instances, XRapplication 1814 may update remote XR environment portion 2232 toreflect the position change of remote device 1810-1. Additionally oralternatively, the position change of remote device 1810-1 does notmodify the view of the scene as seen by other remote participants or bythe host user.

As shown by FIG. 22D, view 2240 displays remote XR environment portion2232 at a later time during the remote collaboration session. View 2240includes remote XR environment portion 2232, asset 2234, host deviceavatar 2242, pin 2244, and map 2246. During the remote collaborationsession, the remote user may implement one or more collaboration toolsin order to navigate through remote XR environment 1930 and/or interactwith portions of the adaptable 3D representation of the physical space.

For example, the remote user may toggle avatars of other participants inthe remote collaboration session to determine the position of eachparticipant's device relative to the physical space. In one example, theremote user may turn on host avatar 2242 in order to see the position ofhost device 1804 and determine what portion of real-world asset 2154(corresponding to rendered asset 2234) that the host user is viewing.The remote user may then tell the user to move to a different positionin order to view a different portion of the real-world asset 2154.

In some embodiments, the remote user may implement collaboration toolsthat are displayed in the environments of other participants. Forexample, the remote user may add a pin 2244 to flag a particular portionof rendered asset 2234 throughout the remote collaboration session. Insuch instances, other participants may see pin 2244 within therespective environments. For example, when host device 1804 changesposition to be located to the right side of real-world asset 2154, hostdevice 1804 may present pin 2244 for display within host XR applicationportion 2152.

In various embodiments, XR application 1814 may generate a map 2246 ofthe scene within view 2240. In such instances, the remote user may moveremote device 1810-1 to change the position of remote device 1810-1within the map of the scene. In some embodiments, the remote device maybe presented with navigation controls. For example, when mobileoperations application 1816 generates a non-XR remote environment 1940for a remote collaboration session, mobile operations application 1816may present navigation controls to change the position of remote device1810-2 within the scene.

FIG. 23 sets forth a flow diagram 2300 of method steps for providing anextended reality stream for a remote collaboration session, inaccordance with example implementations. Although the method steps aredescribed in conjunction with FIGS. 1-22 , persons of ordinary skill inthe art will understand that any system configured to perform thismethod and/or other methods described herein, in any order, and in anycombination not logically contradicted, is within the scope of thepresent invention.

As shown in by method 2300, at step 2301, host device 1804 receivessensor data from a depth sensor 1924. In various embodiments, hostdevice 1804 may receive depth sensor data that was acquired by a depthsensor 1924 (e.g., one or more LiDAR sensors) that are associated withhost device 1804. For example, a LiDAR sensor included in host device1804 may scan a physical space and acquire depth sensor data for thephysical space. XR application 1814 included in host device 1804 mayreceive the depth sensor data and generate 3D depth data that is basedon the depth sensor data.

At step 2303, host device 1804 receives sensor data from an imagingsensor 1926. In various embodiments, host device 1804 may receive imagesensor data that was acquired by an imaging sensor 1926 (e.g., one ormore RGB cameras) that are associated with host device 1804. Forexample, an RGB camera included in host device 1804 may scan thephysical space and acquire image sensor data for the physical space. XRapplication 1814 included in host device 1804 may receive the imagesensor data and generate 2D surface data that is based on the imagesensor data.

At step 2305, host device 1804 determines whether a scan of the physicalspace was successful. In some embodiments, XR application 1814 maydetermine whether host device 1804 successfully acquired both imagesensor data and depth sensor data for the physical space. In someembodiments, XR application 1814 may perform a series of successivechecks to determine whether host device 1804 successfully acquired bothimage sensor data and depth sensor data for specific regions of thephysical space. When host device 1804 determines that host device 1804did not successfully complete the scan of the physical space, hostdevice returns to step 2301, where XR application 1814 prompts the hostuser to rescan at least a portion of the physical space. Otherwise, XRapplication determines that host device 1804 successfully scanned thephysical space and proceeds to step 2307.

At step 2307, host device 1804 combines the 3D depth data and 2D surfacedata to generate an XR stream. In some embodiments, host device 1804 maygenerate the 3D depth data and 2D surface data as a discrete scene thatcan be produced in a non-XR environment. In various embodiments, XRapplication 1814 may combine correlated portions of the 3D depth dataand the 2D surface data to generate the XR stream. In some embodiments,XR application 1814 may determine a correlation between portions of 2Dsurface data and 3D depth data (e.g., determining related sets ofcoordinate data between a portion of 2D surface data and a portion of 3Ddepth data).

At step 2309, host device 1804 transmits the XR stream to one or moreremote devices 1810. In various embodiments, XR application 1814 maytransmit the XR stream to one or more recipients (e.g., remote devices1810, remote storage 1904, etc.) for use in a remote collaborationsession. In some embodiments, XR application 1814 may transmit the XRstream as a stream of the combined 2D surface data and 3D depth data. Insuch instances, the recipient (e.g., remote device 1810-1) may extractthe 2D surface data and 3D depth data to generate an adaptable 3Drepresentation of the physical space corresponding to a scene of thephysical space. Alternatively, host device 1804 may initially generatethe adaptable representation of the physical space and may transmit theadaptable 3D representation to one or more recipients. In variousembodiments, remote device 1810 may generate a remote environment thatincludes at least a portion of the adaptable 3D representation of thephysical space.

FIG. 24 sets forth a flow diagram 2400 of method steps for generatingand interacting with a digital representation of a physical space, inaccordance with example implementations.

Although the method steps are described in conjunction with FIGS. 1-22 ,persons of ordinary skill in the art will understand that any systemconfigured to perform this method and/or other methods described herein,in any order, and in any combination not logically contradicted, iswithin the scope of the present invention.

As shown in method 2400, at step 2401, remote device 1810 receives an XRstream originating from host device 1804. In various embodiments remotedevice 1810 receives an XR stream via tunnel bridge 1906. In someembodiments, the XR stream may include continual chunks of combined 3Ddepth data and/or 2D surface data. Additionally or alternatively, the XRstream may include a discrete set of data that corresponds to a scene ata particular time (e.g., an initial set of geometry data at the start ofthe remote collaboration session). In some embodiments, workspaceservice 1908 may specify that remote device is an intended recipient ofthe XR stream. In such instances, data processing service 1902 maydirect a copy of the XR stream, originating at host device 1804, toremote device 1810.

At step 2403, remote device 1810 renders an adaptable 3D representationof the physical space based on the XR stream. In various embodiments,remote device 1810 may render at least a portion of an adaptablerepresentation of the physical space that is to be used during theremote collaboration session. For example, XR application 1814 includedin remote device 1810-1 may generate the adaptable 3D representation forinclusion in remote XR environment 1930. In another example, remoteoperations application 1816 included in remote device 1810-2 maygenerate the adaptable 3D representation for inclusion in remoteenvironment 1940. In various embodiments, when rendering adaptable 3Drepresentation, remote device 1810 may extract the 2D surface data and3D depth data that is included in the XR stream to generate an adaptable3D representation of the physical space corresponding to a scene of thephysical space.

At step 2405, remote device 1810 receives an input representing aninteraction. In various embodiments, remote device 1810 may receive aninput from a remote user during the remote collaboration session. Forexample, remote device 1810 may receive an input corresponding to theremote user highlighting a portion of the remote environment. In suchinstances, remote device 1810 may determine the interactioncorresponding to the input.

At step 2407, remote device 1810 determines whether to send aninteraction to a recipient. In various embodiments, remote device 1810may determine whether to publish the interaction corresponding to thereceived input to other participants in the remote collaborationsession. For example, XR application 1814 may determine whether theinput corresponding to highlighting a portion of the remote XRenvironment 1930 to be seen by other participants. When remote device1810 determines that the interaction is to be published to otherparticipants, remote device 1810 may proceed to step 2409. Otherwise,remote device 1810 determines that the interaction is not to bepublished to other participants and proceeds to step 2411.

At step 2409, remote device 1810 transmits data based on the receivedinteraction. In various embodiments, remote device 1810 transmits one ormore messages that corresponds to the interaction received by remotedevice 1810. In such instances, remote device 1810 may send the messagethat includes the interaction and associated data (e.g., type ofinteraction, coordinates for the interaction, etc.) via tunnel bridge1906 to workspace service 1908 and/or host device 1804. In suchinstances, other participants may update a corresponding workspace toreflect the interaction (e.g., host device 1804 updating host XRenvironment 1910 to highlight a corresponding portion of XR workspace1914).

At step 2411, remote device 1810 updates the remote environment based onthe received interaction. In various embodiments, remote device 1810updates the remote environment to reflect the interaction correspondingto the received input. For example, XR application 1814 may update XRworkspace 1934 included in remote XR workspace 1930 to highlight aspecific portion corresponding to the user input.

In various embodiments, remote device 1810 may continually update theremote workspace based on the updated XR stream and/or additional inputsprovided by the remote user. In such instances, remote device 1810 mayupdate the remote workspace to reflect the updates.

3.2 Storing Remote Collaboration Session Data

FIG. 25 illustrates a call flow diagram 2500 showing interactionsbetween various components of the example networked computingenvironment 1900, in accordance with example implementations. One ormore components of networked computing environment 1900 may performvarious operations to register a device to receive data associated withan asset, as well as join devices to receive portions of an XR streamused during a remote collaboration session. It is noted that in someembodiments, some or all of the steps shown in FIGS. 25 and 26 may beeliminated or ordered differently, without changing the operation of theclaimed implementations.

When setting up a remote collaboration session, host device 1804 sends ajoin request message 2502 to data processing service 1902. The joinrequest message 2502 includes a session identifier associated with aspecific remote collaboration session.

When host device 1804 initializes a new remote collaboration session,data processing service 1902 responds by generating a new session thatincludes the session identifier provided in the join request message.Data processing service 1902 generates a new subscription message 2504indicating that host device 1804 is authorized to be a participant inthe session specified by the session ID. Data intake and query system108 responds by sending a message 2506, containing a subscriptionidentifier for host device 1804, to data processing service 1902. Dataprocessing service 1902 may then send a status code 2510 to host device1804 confirming the initiation of the session.

When other devices (e.g., remote device 1810-1, 1810-2) subsequentlysend join requests messages with a session identifier matching thesession identifier provided by host device 1804, data processing service1902 may respond by generating new subscriptions for the specific remotecollaboration session. During the remote collaboration session, dataprocessing service 1902 may forward updates and/or other messages (e.g.,interactions, position data, etc.) to other participants in the remotecollaboration session.

During the remote collaboration session, host device 1804 may send adashboard request message 2522 to request a specific dashboard and/orspecific data values associated with a real-world asset 1912. In variousembodiments, dashboard request 2522 includes parameters that specifiesthe data values that are to be included in a response to request 2522.Data intake and query system 108 performs actions 2524 to process thedashboard request 2522 and retrieves a data set from one or more datasources 206 and/or one or more data stores 208 based on informationincluded in dashboard request 2526.

Data intake and query system 108 transmits a dashboard response message2526 that includes the retrieved data set. In some embodiments, theretrieved data set includes a dashboard that includes a set ofvisualizations. In some embodiments, the retrieved data set includes aset of updated values that reflect a specific period of values (e.g., aperiodic update of current conditions). Host device 1804 performsactions 2528 to provide one or more dashboards during the remotecollaboration session. For example, XR application 1814 may retrieve oneor more dashboards and generate visualization to provide in XR workspace1914.

When host device 1804 provides the XR stream, host device 1804 initiallyperforms actions to acquire 2D surface data and 3D depth data 2532. Forexample, host device 1804 may initially scan a physical space.Alternatively, host device 1804 may send updates by re-scanning aportion of the physical space. Based on the scan, XR application 1814generates 2D surface data and 3D depth data associated with the scene.

Host device 1804 transmits the XR stream to data processing service1902. In various embodiments, host device 1804 may transmit the XRstream as a set of serialized chunks 2534. For example, tunnel bridge1906 may limit any given chunk traversing through tunnel bridge 1906 toa maximum size (e.g., 1 MB). In such instances, host device 1804 maybreak the XR stream into a set of serialized chunks 2534 (e.g., XRstream chunks 2534-1, 2534-2, 2534-3) and separately transmits each ofthe serialized chunks 2534 to data processing service 1902. Dataprocessing service 1902 may forward the XR stream to a set ofrecipients, as specified by devices that joined the session using thespecific session ID. In some embodiments, data processing service 1902may store the XR stream in remote storage 1904. Upon receiving each ofthe serialized chunks 2534 for the XR stream, data processing service1902 sends a status code to host device 1804 indicating that theserialized chunks 2534 were successfully received.

FIG. 26 illustrates a call flow diagram 2600 showing interactionsbetween various components of the example networked computingenvironment 1900, in accordance with example implementations. One ormore components of networked computing environment 1900 may performvarious operations to receive data associated with an asset, as well asreceive updates during a remote collaboration session.

During the remote collaboration session, remote device 1810 may send adashboard request message 2602 to request a specific dashboard and/orspecific data values associated with a real-world asset 1912. In someembodiments, remote device 1810 sends dashboard request 2602 to dataintake and query system 108 independent of host device 1804 sendingdashboard request 2522. In various embodiments, dashboard request 2602includes parameters that specifies the data values that are to beincluded in a response to request 2602. Data intake and query system 108performs actions 2604 to process the dashboard request 2602 andretrieves a data set from one or more data sources 206 and/or one ormore data stores 208 based on information included in dashboard request2526.

Data intake and query system 108 transmits a dashboard response message2606 that includes the retrieved data set. In some embodiments, theretrieved data set includes a dashboard that includes a set ofvisualizations. In some embodiments, the retrieved data set includes aset of updated values that reflect a specific period of values (e.g., aperiodic update of current conditions). Remote device 1810 performsactions 2608 to provide one or more dashboards during the remotecollaboration session. For example, XR application 1814 may retrieve oneor more dashboards and generate visualization to provide in remote XRworkspace 1934.

At various points during the remote collaboration session, one or moredevices 1804, 1810 may perform actions that update the environment. Forexample, host device 1804 may re-scan a portion of the physical space,while host device 1804 and/or remote device 1810 may perform aninteraction that modifies the remote collaboration environment (e.g.,add an annotation, place an AR element, etc.).

When host device 1804 re-scans a portion of the physical space, hostdevice 1804 sends to data processing service 1902 an environment updatemessage 2622 that includes updated 2D surface data and 3D depth data. Insome embodiments, environment update message 2622 may be a set ofserialized chunks (e.g., 2622-1, 2622-2, 2622-3). When remote device1810 performs an interaction (e.g., use a collaboration tool, modify thestate of real-world asset 1912), remote device 1810 sends interactionupdated message 2623 to data processing service 1902.

Data processing service 1902 performs actions 2624 to store the updatesas received from host device 1804 and/or remote device 1810. Forexample, data processing service 1902 may generate and update anactivity log that records the interaction updates specified by theinteraction update message 2623. In another example, data processingservice 1902 may store one or more delta files that store theenvironment update as a change to the initial XR stream 2534. In suchinstances, a participant joining the remote collaboration session maysync with the other participants by receiving the initial XR stream 2534and the environment update 2622 from data processing service (e.g.,downloading the data from remote storage 1904).

Once data processing service 1902 stores the updates, data processingservice 1902 may broadcast the updates to each participant in the remotecollaboration session. For example, data processing service 1902 maybroadcast environment update 2622 as a broadcast update 2626 to remotedevice 1810 (when environment update 2622 is serialized, data processingservice 1902 may also serialize the broadcast to the other participantsin the remote collaboration session). Once data processing service 1902broadcasts the update to the other participants, data processing service1902 may send a status update to host device 1804 stating that theupdate was successfully transmitted to the other participants.

In some embodiments, a participant may record the remote collaborationsession. At a later time, a user may use a device (e.g., remote device1810-2) to replay the recorded remote collaboration session. Forexample, a QA expert may replay the remote collaboration session toreview how an expert discussed an issue with a requester. In suchinstances, the replay device may retrieve a recorded remotecollaboration session from remote storage 1904. When storing the remotecollaboration session, data processing service 1902 may store the XRstream (containing the combined 2D and 3D data) in lieu of a video ofthe remote collaboration session. In such instances, the stored XRstream includes correlated 2D surface data and 3D depth data that ismuch smaller in data size than an equivalent high-definition video clip.In some embodiments, the stored remote collaboration session alsoincludes an activity log that records actions performed by each user(and stored based on the time performed) and/or audio data.

When replaying the session, the replay device renders the scene as anadaptable 3D representation of the physical space. In such instances,the user may move the replay device to view different portions of thescene. Because the replay device generates the entire adaptable 3Drepresentation, the user may navigate through the scene independent ofhow the participants navigated through the scene during the recordedremote collaboration session. In some embodiments, the user may chooseto review the recorded remote collaboration session from the perspectiveof a particular participant. In such instances, the replay device mayretrieve position data from the stored XR stream and present theadaptable 3D representation from the perspective of the participantselected by the user.

FIG. 27 sets forth a flow diagram 2700 of method steps for providing arecorded remote collaboration session, in accordance with exampleimplementations. Although the method steps are described in conjunctionwith FIGS. 1-26 , persons of ordinary skill in the art will understandthat any system configured to perform this method and/or other methodsdescribed herein, in any order, and in any combination not logicallycontradicted, is within the scope of the present invention.

Method 2700 begins at steps 2701, where host device 1804 receives sensordata from a depth sensor 1924. In various embodiments, host device 1804may receive depth sensor data that was acquired by a depth sensor 1924(e.g., one or more LiDAR sensors) that are associated with host device1804. For example, a LiDAR sensor included in host device 1804 may scana physical space and acquire depth sensor data for the physical space.XR application 1814 included in host device 1804 may receive the depthsensor data and generate 3D depth data that is based on the depth sensordata.

At step 2703, host device 1804 receives sensor data from an imagingsensor 1926. In various embodiments, host device 1804 may receive imagesensor data that was acquired by an imaging sensor 1926 (e.g., one ormore RGB cameras) that are associated with host device 1804. Forexample, an RGB camera included in host device 1804 may scan thephysical space and acquire image sensor data for the physical space. XRapplication 1814 included in host device 1804 may receive the imagesensor data and generate 2D surface data that is based on the imagesensor data.

At step 2705, host device 1804 combines the 3D depth data and 2D surfacedata to generate an XR stream. In some embodiments, host device 1804 maygenerate the 3D depth data and 2D surface data as a discrete scene thatcan be produced in a non-XR environment. In various embodiments, XRapplication 1814 may combine correlated portions of the 3D depth dataand the 2D surface data to generate the XR stream. In some embodiments,XR application 1814 may determine a correlation between portions of 2Dsurface data and 3D depth data (e.g., determining related sets ofcoordinate data between a portion of 2D surface data and a portion of 3Ddepth data).

At step 2707, host device 1804 adds one or more AR elements and/orinteractions to the XR stream. In various embodiments, host device 1804may add additional data to the XR stream. For example, host device 1804may add an indication of the position of host device 1804 relative toreal-world asset 1912. In another example, host device 1804 may includethe addition of a collaboration tool, such as a pin or an annotation, toa position within XR workspace 1914. In such instances, XR application1814 included in host device 1804 may add the one or more AR elementsand/or interactions to the XR stream.

At step 2709, host device 1804 transmits the XR stream to one or moreremote devices 1810. In various embodiments, XR application 1814 maytransmit the XR stream to one or more recipients (e.g., remote devices1810, remote storage 1904, etc.) for use in a remote collaborationsession. In some embodiments, XR application 1814 may transmit the XRstream as a stream of the combined 2D surface data, 3D depth data, ARelements, and/or interaction data. In such instances, a given recipient(e.g., remote device 1810-1) may extract the contents of the XR streamin order to render a remote environment that includes an adaptable 3Drepresentation of the physical space corresponding to a scene of thephysical space. In various embodiments, the given recipient maycontinually update the remote environment in order to reflect the ARelements and/or interaction data that is also included in the XR stream.

At step 2711, host device 1804 transmits the XR stream to remote storagedevice 1904. In various embodiments, one of the participants of theremote collaboration session may record the remote collaborationsession. In some embodiments, recording the remote collaboration sessionmay include storing the XR stream, an activity log of actions performedby host device 1804 and/or the one or more remote devices 1810, and/orAR elements displayed by host device 1804 and/or the one or more remotedevices 1810 within the respective host XR environment 1910, or remoteXR environment 1930, and/or remote environment 1940. In someembodiments, XR application 1814 may also store an audio recording fromeach respective device included in the remote collaboration session. Invarious embodiments, host device 1804 transmits at least the XR streamto remote storage in order to store the remote collaboration session.

In sum, a host device and one or more remote devices that interact in acommon remote collaboration session. When initiating the remotecollaboration session, an extended reality application in the hostdevice causes sensors associated with the host device to scan a scenethat is a portion of a real-world environment. In various embodiments, adepth sensor acquires three-dimensional depth data, and an imagingsensor acquires two-dimensional surface data for a physical space in thereal-world environment. The extended reality application device combinescorrelated 2D surface data and 3D depth data into an extended realitystream and transmits the extended reality stream to a remote device. Invarious embodiments, the host device may transmit the extended realitystream in a series of serialized chunks.

The remote device receives the extended reality stream and renders,based on the correlated 2D surface data and 3D depth data encapsulatedin the extended reality stream, a portion of the physical space forpresentation at the location of the remote device. In variousembodiments, the remote environment generated by the remote deviceincludes a digital representation of a real-world asset that is includedin the physical space. In various embodiments, the host device and theremote device may independently retrieve data values associated with thereal-world asset and present the data within the respective hostenvironment and remote environment. During the remote collaborationsession, the remote device updates a view of the remote environment tocorrespond to different positions relative to the digital representationof the real-world asset. The remote device updates the view of theremote environment independent of the view that the host device ispresenting to the host user.

In various embodiments, at least one of the host device or the remotedevice may send the extended reality stream and one or more interactionswith the portions of the host environment or the remote environment to aremote storage device to store the remote collaboration session. In suchinstances, a replay device may retrieve the extended reality stream toreplay render the portion of the physical space and the interactionstaken by the host device and the replay device during the remotecollaboration session.

At least one technological advantage of the disclosed techniquesrelative to prior techniques is that remote devices may view andinteract with portions of a digital representation of a digital spaceindependent from the perspective of the host device. In particular, byinitially scanning and providing an extended reality stream thatincludes a combination of correlated 3D depth data and 2D surface data,a remote device can generate a digital reproduction of a physical spacewithout requiring a continual video stream of the physical space from ahost device. In addition, storing the extended reality stream enablesreplay devices to produce a digital reproduction of the physical spaceinstead of viewing a series of video clips, reducing storage spaceassociated with recording a remote collaboration session.

1. In various implementations, a method comprises generating, based onfirst sensor data captured by a depth sensor on a mobile device,three-dimensional data representing a physical space that includes areal-world asset, generating, based on second sensor data captured by animage sensor on the mobile device, two-dimensional data representing thephysical space, generating an extended reality (XR) stream representingat least a portion of a remote collaboration session between a hostdevice and a set of one or more remote devices, where the XR streamincludes a combination of the three-dimensional data and thetwo-dimensional data that includes a digital representation of thereal-world asset, a set of AR elements that are associated with thereal-world asset, and a set of performed actions, each action in the setof performed actions associated with (i) at least a portion of thedigital representation, or (ii) at least one AR element in the set of ARelements, serializing the XR stream into a set of serialized chunks,transmitting the set of serialized chunks to the set of one or moreremote devices, wherein the set of one or more remote devices at leastpartially recreate the XR stream in a set of one or more remote XRenvironments, and transmitting the set of serialized chunks to a remotestorage device, wherein a device subsequently retrieves the set ofserialized chunks to replay the remote collaboration session.

2. The method of clause 1, where a first combined storage size of theset of serialized chunks is smaller than a second combined storage sizeof a corresponding video stream.

3. The method of clause 1 or 2, where the XR stream includes an activitylog that stores reference times for each performed action in the set ofperformed actions.

4. The method of any of clauses 1-3, where the set of serialized chunksincludes an initial set of serialized chunks that correspond to aninitial rendering of the XR stream at an initial time, and one or moredelta sets of serialized chunks that correspond to one or moresubsequent renderings of the XR stream at subsequent times.

5. The method of any of clauses 1-4, where the set of remote devicesinclude at least two remote devices that subscribe to receive the set ofserialized chunks.

6. The method of any of clauses 1-5, where the set of remote devicesincludes a first remote device that sends a request to receive the setof serialized chunks from at least one of a host device or the remotestorage device.

7. The method of any of clauses 1-6, where the XR stream furtherincludes field-of-view data corresponding to viewpoints of the set ofone or more remote devices.

8. The method of any of clauses 1-7, where the set of AR elementsincludes a set of one or more visual display panels positioned relativeto the digital representation, and a set of one or more collaborationtools that modify an appearance of at least a portion of the set of oneor more remote XR environments.

9. In various implementations, one or more non-transitorycomputer-readable media store instructions that, when executed by one ormore processors, cause the one or more processors to perform the stepsof generating, based on first sensor data captured by a depth sensor ona mobile device, three-dimensional data representing a physical spacethat includes a real-world asset, generating, based on second sensordata captured by an image sensor on the mobile device, two-dimensionaldata representing the physical space, generating an extended reality(XR) stream representing at least a portion of a remote collaborationsession between a host device and a set of one or more remote devices,where the XR stream includes a combination of the three-dimensional dataand the two-dimensional data that includes a digital representation ofthe real-world asset, a set of AR elements that are associated with thereal-world asset, and a set of performed actions, each action in the setof performed actions associated with (i) at least a portion of thedigital representation, or (ii) at least one AR element in the set of ARelements, serializing the XR stream into a set of serialized chunks,transmitting the set of serialized chunks to the set of one or moreremote devices, wherein the set of one or more remote devices at leastpartially recreate the XR stream in a set of one or more remote XRenvironments, and transmitting the set of serialized chunks to a remotestorage device, wherein a device subsequently retrieves the set ofserialized chunks to replay the remote collaboration session.

10. The one or more non-transitory computer readable media of clause 9,where a first combined storage size of the set of serialized chunks issmaller than a second combined storage size of a corresponding videostream.

11. The one or more non-transitory computer readable media of clause 9or 10, where the XR stream includes an activity log that storesreference times for each performed action in the set of performedactions.

12. The one or more non-transitory computer readable media of any ofclauses 9-11, where the set of serialized chunks includes an initial setof serialized chunks that correspond to an initial rendering of the XRstream at an initial time, and one or more delta sets of serializedchunks that correspond to one or more subsequent renderings of the XRstream at subsequent times.

13. The one or more non-transitory computer readable media of any ofclauses 9-12, where the set of remote devices include at least tworemote devices that subscribe to receive the set of serialized chunks.

14. The one or more non-transitory computer readable media of any ofclauses 9-13, where the set of remote devices includes a first remotedevice that sends a request to receive the set of serialized chunks fromat least one of a host device or the remote storage device.

15. The one or more non-transitory computer readable media of any ofclauses 9-14, where the XR stream further includes field-of-view datacorresponding to viewpoints of the set of one or more remote devices.

16. The one or more non-transitory computer readable media of any ofclauses 9-15, where the set of AR elements includes a set of one or morevisual display panels positioned relative to the digital representation,and a set of one or more collaboration tools that modify an appearanceof at least a portion of the set of one or more remote XR environments.

17. In various implementations, a system comprises a memory storing anextended reality application, and a processor, coupled to the memory,that executes the extended reality application by performing the stepsof generating, based on first sensor data captured by a depth sensor ona mobile device, three-dimensional data representing a physical spacethat includes a real-world asset, generating, based on second sensordata captured by an image sensor on the mobile device, two-dimensionaldata representing the physical space, generating an extended reality(XR) stream representing at least a portion of a remote collaborationsession between a host device and a set of one or more remote devices,where the XR stream includes a combination of the three-dimensional dataand the two-dimensional data that includes a digital representation ofthe real-world asset, a set of AR elements that are associated with thereal-world asset, and a set of performed actions, each action in the setof performed actions associated with (i) at least a portion of thedigital representation, or (ii) at least one AR element in the set of ARelements, serializing the XR stream into a set of serialized chunks,transmitting the set of serialized chunks to the set of one or moreremote devices, wherein the set of one or more remote devices at leastpartially recreate the XR stream in a set of one or more remote XRenvironments, and transmitting the set of serialized chunks to a remotestorage device, wherein a device subsequently retrieves the set ofserialized chunks to replay the remote collaboration session.

18. The system of clause 17, where a first combined storage size of theset of serialized chunks is smaller than a second combined storage sizeof a corresponding video stream.

19. The system of clause 17 or 18, where the XR stream includes anactivity log that stores reference times for each performed action inthe set of performed actions.

20. The system of any of clauses 17-19, where the set of serializedchunks includes an initial set of serialized chunks that correspond toan initial rendering of the XR stream at an initial time, and one ormore delta sets of serialized chunks that correspond to one or moresubsequent renderings of the XR stream at subsequent times.

Any and all combinations of any of the claim elements recited in any ofthe claims and/or any elements described in this application, in anyfashion, fall within the contemplated scope of the present invention andprotection.

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments.

Aspects of the present embodiments may be embodied as a system, methodor computer program product. Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.) or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “module” or“system.” In addition, any hardware and/or software technique, process,function, component, engine, module, or system described in the presentdisclosure may be implemented as a circuit or set of circuits.Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

Aspects of the present disclosure are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, enable the implementation of the functions/acts specified inthe flowchart and/or block diagram block or blocks. Such processors maybe, without limitation, general purpose processors, special-purposeprocessors, application-specific processors, or field-programmable gatearrays.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent one or more modules, segments,or portions of code, which each comprise one or more executableinstructions for implementing the specified logical function(s). Itshould also be noted that, in some alternative implementations, thefunctions noted in the block may occur out of the order noted in thefigures. For example, two blocks shown in succession may, in fact, beexecuted substantially concurrently, or the blocks may sometimes beexecuted in the reverse order, depending upon the functionalityinvolved. It will also be noted that each block of the block diagramsand/or flowchart illustration, and combinations of blocks in the blockdiagrams and/or flowchart illustration, can be implemented by specialpurpose hardware-based systems that perform the specified functions oracts, or combinations of special purpose hardware and computerinstructions.

While the preceding is directed to embodiments of the presentdisclosure, other and further embodiments of the disclosure may bedevised without departing from the basic scope thereof, and the scopethereof is determined by the claims that follow.

What is claimed is:
 1. A method comprising: generating, based on firstsensor data captured by a depth sensor on a mobile device,three-dimensional data representing a physical space that includes areal-world asset; generating, based on second sensor data captured by animage sensor on the mobile device, two-dimensional data representing thephysical space; generating an extended reality (XR) stream representingat least a portion of a remote collaboration session between a hostdevice and a set of one or more remote devices, wherein the XR streamincludes: a combination of the three-dimensional data and thetwo-dimensional data that includes a digital representation of thereal-world asset, a set of augmented reality (AR) elements that areassociated with the real-world asset, and a set of performed actions,each action in the set of performed actions associated with (i) at leasta portion of the digital representation, or (ii) at least one AR elementin the set of AR elements; serializing the XR stream into a set ofserialized chunks, wherein the set of serialized chunks comprises: aninitial set of serialized chunks that correspond to an initial renderingof the XR stream at an initial time, and one or more delta sets ofserialized chunks that correspond to one or more subsequent renderingsof the XR stream at subsequent times; transmitting the set of serializedchunks to the set of one or more remote devices, wherein the set of oneor more remote devices at least partially recreate the XR stream in aset of one or more remote XR environments; and transmitting the set ofserialized chunks to a remote storage device, wherein a devicesubsequently retrieves the set of serialized chunks to replay the remotecollaboration session.
 2. The method of claim 1, wherein a firstcombined storage size of the set of serialized chunks is smaller than asecond combined storage size of a corresponding video stream.
 3. Themethod of claim 1, wherein the XR stream includes an activity log thatstores reference times for each performed action in the set of performedactions.
 4. The method of claim 1, wherein the set of remote devicesinclude at least two remote devices that subscribe to receive the set ofserialized chunks.
 5. The method of claim 1, wherein the set of remotedevices includes a first remote device that sends a request to receivethe set of serialized chunks from at least one of a host device or theremote storage device.
 6. The method of claim 1, wherein the XR streamfurther includes field-of-view data corresponding to viewpoints of theset of one or more remote devices.
 7. The method of claim 1, wherein theset of AR elements includes: a set of one or more visual display panelspositioned relative to the digital representation; and a set of one ormore collaboration tools that modify an appearance of at least a portionof the set of one or more remote XR environments.
 8. One or morenon-transitory computer-readable media storing instructions that, whenexecuted by one or more processors, cause the one or more processors toperform the steps of: generating, based on first sensor data captured bya depth sensor on a mobile device, three-dimensional data representing aphysical space that includes a real-world asset; generating, based onsecond sensor data captured by an image sensor on the mobile device,two-dimensional data representing the physical space; generating anextended reality (XR) stream representing at least a portion of a remotecollaboration session between a host device and a set of one or moreremote devices, wherein the XR stream includes: a combination of thethree-dimensional data and the two-dimensional data that includes adigital representation of the real-world asset, a set of augmentedreality (AR) elements that are associated with the real-world asset, anda set of performed actions, each action in the set of performed actionsassociated with (i) at least a portion of the digital representation, or(ii) at least one AR element in the set of AR elements; serializing theXR stream into a set of serialized chunks, wherein the set of serializedchunks comprises: an initial set of serialized chunks that correspond toan initial rendering of the XR stream at an initial time, and one ormore delta sets of serialized chunks that correspond to one or moresubsequent renderings of the XR stream at subsequent times; transmittingthe set of serialized chunks to the set of one or more remotetransmitting the set of serialized chunks to the set of one or moreremote devices, wherein the set of one or more remote devices at leastpartially recreate the XR stream in a set of one or more remote XRenvironments; and transmitting the set of serialized chunks to a remotestorage device, wherein a device subsequently retrieves the set ofserialized chunks to replay the remote collaboration session.
 9. The oneor more non-transitory computer readable media of claim 8, wherein afirst combined storage size of the set of serialized chunks is smallerthan a second combined storage size of a corresponding video stream. 10.The one or more non-transitory computer readable media of claim 8,wherein the XR stream includes an activity log that stores referencetimes for each performed action in the set of performed actions.
 11. Theone or more non-transitory computer readable media of claim 8, whereinthe set of remote devices include at least two remote devices thatsubscribe to receive the set of serialized chunks.
 12. The one or morenon-transitory computer readable media of claim 8, wherein the set ofremote devices includes a first remote device that sends a request toreceive the set of serialized chunks from at least one of a host deviceor the remote storage device.
 13. The one or more non-transitorycomputer readable media of claim 8, wherein the XR stream furtherincludes field-of-view data corresponding to viewpoints of the set ofone or more remote devices.
 14. The one or more non-transitory computerreadable media of claim 8, wherein the set of AR elements includes: aset of one or more visual display panels positioned relative to thedigital representation; and a set of one or more collaboration toolsthat modify an appearance of at least a portion of the set of one ormore remote XR environments.
 15. A system comprising: a memory storingan extended reality application; and a processor, coupled to the memory,that executes the extended reality application by performing the stepsof: generating, based on first sensor data captured by a depth sensor ona mobile device, three-dimensional data representing a physical spacethat includes a real-world asset; generating, based on second sensordata captured by an image sensor on the mobile device, two-dimensionaldata representing the physical space; generating an extended reality(XR) stream representing at least a portion of a remote collaborationsession between a host device and a set of one or more remote devices,wherein the XR stream includes: a combination of the three-dimensionaldata and the two-dimensional data that includes a digital representationof the real-world asset, a set of augmented reality (AR) elements thatare associated with the real-world asset, and a set of performedactions, each action in the set of performed actions associated with (i)at least a portion of the digital representation, or (ii) at least oneAR element in the set of AR elements; serializing the XR stream into aset of serialized chunks, wherein the set of serialized chunkscomprises: an initial set of serialized chunks that correspond to aninitial rendering of the XR stream at an initial time, and one or moredelta sets of serialized chunks that correspond to one or moresubsequent renderings of the XR stream at subsequent times; transmittingthe set of serialized chunks to the set of one or more remote;transmitting the set of serialized chunks to the set of one or moreremote devices, wherein the set of one or more remote devices at leastpartially recreate the XR stream in a set of one or more remote XRenvironments; and transmitting the set of serialized chunks to a remotestorage device, wherein a device subsequently retrieves the set ofserialized chunks to replay the remote collaboration session.
 16. Thesystem of claim 15, wherein a first combined storage size of the set ofserialized chunks is smaller than a second combined storage size of acorresponding video stream.
 17. The system of claim 15, wherein the XRstream includes an activity log that stores reference times for eachperformed action in the set of performed actions.